Overview

Dataset statistics

Number of variables9
Number of observations38
Missing cells4
Missing cells (%)1.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 KiB
Average record size in memory79.5 B

Variable types

Categorical5
Text2
Numeric2

Dataset

Description2017~2020년도 충청남도 보령시 지방세 관련 과세액 중 비과세액과 감면액이 차지하는 비율 현황 항목에 대한 자료를 제공합니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=350&beforeMenuCd=DOM_000000201001001000&publicdatapk=15079084

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
감면금액 is highly overall correlated with 비과세감면율 and 1 other fieldsHigh correlation
비과세감면율 is highly overall correlated with 감면금액High correlation
세목명 is highly overall correlated with 감면금액High correlation
비과세금액 has 4 (10.5%) missing valuesMissing
감면금액 has unique valuesUnique
비과세감면율 has 8 (21.1%) zerosZeros

Reproduction

Analysis started2024-01-09 20:18:50.865732
Analysis finished2024-01-09 20:18:51.524029
Duration0.66 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size436.0 B
충청남도
38 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
충청남도 38
100.0%

Length

2024-01-10T05:18:51.576525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:18:51.657587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청남도 38
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size436.0 B
보령시
38 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
보령시 38
100.0%

Length

2024-01-10T05:18:51.742638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:18:51.817638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
보령시 38
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size436.0 B
44180
38 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row44180
2nd row44180
3rd row44180
4th row44180
5th row44180

Common Values

ValueCountFrequency (%)
44180 38
100.0%

Length

2024-01-10T05:18:51.897207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:18:51.973240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
44180 38
100.0%

세목명
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Memory size436.0 B
등록세
5 
재산세
5 
주민세
5 
취득세
5 
자동차세
5 
Other values (3)
13 

Length

Max length7
Median length3
Mean length3.8157895
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row교육세
2nd row등록세
3rd row재산세
4th row주민세
5th row취득세

Common Values

ValueCountFrequency (%)
등록세 5
13.2%
재산세 5
13.2%
주민세 5
13.2%
취득세 5
13.2%
자동차세 5
13.2%
등록면허세 5
13.2%
교육세 4
10.5%
지역자원시설세 4
10.5%

Length

2024-01-10T05:18:52.061322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:18:52.163202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
등록세 5
13.2%
재산세 5
13.2%
주민세 5
13.2%
취득세 5
13.2%
자동차세 5
13.2%
등록면허세 5
13.2%
교육세 4
10.5%
지역자원시설세 4
10.5%

과세년도
Categorical

Distinct5
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Memory size436.0 B
2017
8 
2018
8 
2019
8 
2021
8 
2020
6 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2017
3rd row2017
4th row2017
5th row2017

Common Values

ValueCountFrequency (%)
2017 8
21.1%
2018 8
21.1%
2019 8
21.1%
2021 8
21.1%
2020 6
15.8%

Length

2024-01-10T05:18:52.271851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:18:52.359225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 8
21.1%
2018 8
21.1%
2019 8
21.1%
2021 8
21.1%
2020 6
15.8%

비과세금액
Text

MISSING 

Distinct31
Distinct (%)91.2%
Missing4
Missing (%)10.5%
Memory size436.0 B
2024-01-10T05:18:52.512577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length7.7058824
Min length1

Characters and Unicode

Total characters262
Distinct characters11
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

Unique30 ?
Unique (%)88.2%

Sample

1st row
2nd row7883535000
3rd row56156000
4th row1232886000
5th row54917000
ValueCountFrequency (%)
7883535000 1
 
3.3%
56156000 1
 
3.3%
12266000 1
 
3.3%
55192000 1
 
3.3%
3006081000 1
 
3.3%
27200000 1
 
3.3%
9308662000 1
 
3.3%
0 1
 
3.3%
10659600 1
 
3.3%
45667970 1
 
3.3%
Other values (20) 20
66.7%
2024-01-10T05:18:52.782880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 94
35.9%
5 24
 
9.2%
1 23
 
8.8%
6 22
 
8.4%
8 20
 
7.6%
3 17
 
6.5%
2 15
 
5.7%
4 14
 
5.3%
9 13
 
5.0%
7 12
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 254
96.9%
Space Separator 8
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 94
37.0%
5 24
 
9.4%
1 23
 
9.1%
6 22
 
8.7%
8 20
 
7.9%
3 17
 
6.7%
2 15
 
5.9%
4 14
 
5.5%
9 13
 
5.1%
7 12
 
4.7%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 262
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 94
35.9%
5 24
 
9.2%
1 23
 
8.8%
6 22
 
8.4%
8 20
 
7.6%
3 17
 
6.5%
2 15
 
5.7%
4 14
 
5.3%
9 13
 
5.0%
7 12
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 262
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 94
35.9%
5 24
 
9.2%
1 23
 
8.8%
6 22
 
8.4%
8 20
 
7.6%
3 17
 
6.5%
2 15
 
5.7%
4 14
 
5.3%
9 13
 
5.0%
7 12
 
4.6%

감면금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3377786 × 109
Minimum2000
Maximum1.2190641 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2024-01-10T05:18:52.924029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile3850
Q115748315
median1.53881 × 108
Q31.4922435 × 109
95-th percentile6.2668863 × 109
Maximum1.2190641 × 1010
Range1.2190639 × 1010
Interquartile range (IQR)1.4764952 × 109

Descriptive statistics

Standard deviation2.6785352 × 109
Coefficient of variation (CV)2.002226
Kurtosis7.5953598
Mean1.3377786 × 109
Median Absolute Deviation (MAD)1.527225 × 108
Skewness2.7135775
Sum5.0835588 × 1010
Variance7.1745508 × 1018
MonotonicityNot monotonic
2024-01-10T05:18:53.030895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
3000 1
 
2.6%
163486000 1
 
2.6%
166464000 1
 
2.6%
76866000 1
 
2.6%
15741570 1
 
2.6%
1914158670 1
 
2.6%
15768550 1
 
2.6%
5142072960 1
 
2.6%
535903440 1
 
2.6%
6000 1
 
2.6%
Other values (28) 28
73.7%
ValueCountFrequency (%)
2000 1
2.6%
3000 1
2.6%
4000 1
2.6%
6000 1
2.6%
2311000 1
2.6%
5418000 1
2.6%
14593000 1
2.6%
15023000 1
2.6%
15207000 1
2.6%
15741570 1
2.6%
ValueCountFrequency (%)
12190641000 1
2.6%
8512112000 1
2.6%
5870670000 1
2.6%
5687223000 1
2.6%
5142072960 1
2.6%
1914158670 1
2.6%
1910654000 1
2.6%
1902865000 1
2.6%
1822230000 1
2.6%
1801686000 1
2.6%

부과금액
Text

Distinct36
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Memory size436.0 B
2024-01-10T05:18:53.203468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length9.6842105
Min length1

Characters and Unicode

Total characters368
Distinct characters11
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

Unique35 ?
Unique (%)92.1%

Sample

1st row11244463000
2nd row
3rd row13979808000
4th row3106859000
5th row44325307000
ValueCountFrequency (%)
11244463000 1
 
2.9%
3863069150 1
 
2.9%
3105740000 1
 
2.9%
14800402000 1
 
2.9%
62914010 1
 
2.9%
23489617730 1
 
2.9%
3750747840 1
 
2.9%
38854434420 1
 
2.9%
4314166000 1
 
2.9%
17595146000 1
 
2.9%
Other values (25) 25
71.4%
2024-01-10T05:18:53.481806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 120
32.6%
1 43
 
11.7%
3 37
 
10.1%
8 28
 
7.6%
9 27
 
7.3%
4 25
 
6.8%
5 25
 
6.8%
6 24
 
6.5%
7 18
 
4.9%
2 15
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 362
98.4%
Space Separator 6
 
1.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 120
33.1%
1 43
 
11.9%
3 37
 
10.2%
8 28
 
7.7%
9 27
 
7.5%
4 25
 
6.9%
5 25
 
6.9%
6 24
 
6.6%
7 18
 
5.0%
2 15
 
4.1%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 368
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 120
32.6%
1 43
 
11.7%
3 37
 
10.1%
8 28
 
7.6%
9 27
 
7.3%
4 25
 
6.8%
5 25
 
6.8%
6 24
 
6.5%
7 18
 
4.9%
2 15
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 368
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 120
32.6%
1 43
 
11.7%
3 37
 
10.1%
8 28
 
7.6%
9 27
 
7.3%
4 25
 
6.8%
5 25
 
6.8%
6 24
 
6.5%
7 18
 
4.9%
2 15
 
4.1%

비과세감면율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)81.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.595
Minimum0
Maximum69.43
Zeros8
Zeros (%)21.1%
Negative0
Negative (%)0.0%
Memory size474.0 B
2024-01-10T05:18:53.611222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.3825
median3.545
Q319.4375
95-th percentile64.824
Maximum69.43
Range69.43
Interquartile range (IQR)18.055

Descriptive statistics

Standard deviation21.047706
Coefficient of variation (CV)1.5481946
Kurtosis1.9583863
Mean13.595
Median Absolute Deviation (MAD)3.545
Skewness1.7796675
Sum516.61
Variance443.00592
MonotonicityNot monotonic
2024-01-10T05:18:53.719545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0.0 8
 
21.1%
69.43 1
 
2.6%
2.03 1
 
2.6%
3.63 1
 
2.6%
3.46 1
 
2.6%
26.29 1
 
2.6%
1.33 1
 
2.6%
65.3 1
 
2.6%
4.51 1
 
2.6%
3.85 1
 
2.6%
Other values (21) 21
55.3%
ValueCountFrequency (%)
0.0 8
21.1%
1.31 1
 
2.6%
1.33 1
 
2.6%
1.54 1
 
2.6%
1.62 1
 
2.6%
1.78 1
 
2.6%
1.98 1
 
2.6%
2.03 1
 
2.6%
2.06 1
 
2.6%
2.37 1
 
2.6%
ValueCountFrequency (%)
69.43 1
2.6%
65.3 1
2.6%
64.74 1
2.6%
64.03 1
2.6%
47.88 1
2.6%
30.28 1
2.6%
26.29 1
2.6%
25.02 1
2.6%
23.46 1
2.6%
20.55 1
2.6%

Interactions

2024-01-10T05:18:51.201554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:18:51.065575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:18:51.278871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:18:51.130742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T05:18:53.816759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도비과세금액감면금액부과금액비과세감면율
세목명1.0000.0000.0000.7721.0000.674
과세년도0.0001.0000.4440.0000.7420.000
비과세금액0.0000.4441.0001.0001.0000.947
감면금액0.7720.0001.0001.0001.0000.904
부과금액1.0000.7421.0001.0001.0001.000
비과세감면율0.6740.0000.9470.9041.0001.000
2024-01-10T05:18:53.954271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명
과세년도1.0000.000
세목명0.0001.000
2024-01-10T05:18:54.056974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
감면금액비과세감면율세목명과세년도
감면금액1.0000.8530.5900.000
비과세감면율0.8531.0000.4320.000
세목명0.5900.4321.0000.000
과세년도0.0000.0000.0001.000

Missing values

2024-01-10T05:18:51.376606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T05:18:51.480167image/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

시도명시군구명자치단체코드세목명과세년도비과세금액감면금액부과금액비과세감면율
0충청남도보령시44180교육세20173000112444630000.0
1충청남도보령시44180등록세2017<NA>152070000.0
2충청남도보령시44180재산세2017788353500018222300001397980800069.43
3충청남도보령시44180주민세201756156000541800031068590001.98
4충청남도보령시44180취득세20171232886000121906410004432530700030.28
5충청남도보령시44180자동차세201754917000561459000150507350004.1
6충청남도보령시44180등록면허세2017441400022510300025978910008.83
7충청남도보령시44180지역자원시설세201715465600091795000138309780001.78
8충청남도보령시44180교육세20182000106110710000.0
9충청남도보령시44180등록세2018<NA>23110000.0
시도명시군구명자치단체코드세목명과세년도비과세금액감면금액부과금액비과세감면율
28충청남도보령시44180자동차세202045667970535903440150892069503.85
29충청남도보령시44180등록면허세20201065960016348600038630691504.51
30충청남도보령시44180교육세202106000119308700000.0
31충청남도보령시44180등록세2021<NA>2348600000.0
32충청남도보령시44180재산세2021930866200019106540001718165200065.3
33충청남도보령시44180주민세2021272000002058900035963890001.33
34충청남도보령시44180취득세2021300608100085121120004381653800026.29
35충청남도보령시44180자동차세202155192000493430000158399380003.46
36충청남도보령시44180등록면허세20211226600014427600043141660003.63
37충청남도보령시44180지역자원시설세202118147900078359000128195380002.03