Overview

Dataset statistics

Number of variables9
Number of observations23
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 KiB
Average record size in memory80.7 B

Variable types

Categorical5
Text3
Numeric1

Dataset

Description2017년부터 2019년 지방세 과세금액 중 비과세금액과 감면액이 차지하는 비율 데이터로 국민 조세 혜택 규모를 파악하는데 사용
Author경상남도 양산시
URLhttps://www.data.go.kr/data/15079425/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
비과세감면율 is highly overall correlated with 세목명High correlation
세목명 is highly overall correlated with 비과세감면율High correlation
비과세감면율 has 5 (21.7%) zerosZeros

Reproduction

Analysis started2023-12-12 16:11:05.593106
Analysis finished2023-12-12 16:11:06.313841
Duration0.72 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size316.0 B
경상남도
23 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상남도 23
100.0%

Length

2023-12-13T01:11:06.364507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:11:06.435938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 23
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size316.0 B
양산시
23 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
양산시 23
100.0%

Length

2023-12-13T01:11:06.511512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:11:06.582216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
양산시 23
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size316.0 B
48330
23 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row48330
2nd row48330
3rd row48330
4th row48330
5th row48330

Common Values

ValueCountFrequency (%)
48330 23
100.0%

Length

2023-12-13T01:11:06.657414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:11:06.765987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48330 23
100.0%

세목명
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)34.8%
Missing0
Missing (%)0.0%
Memory size316.0 B
등록세
3 
재산세
3 
주민세
3 
취득세
3 
자동차세
3 
Other values (3)
8 

Length

Max length7
Median length3
Mean length3.9130435
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
등록세 3
13.0%
재산세 3
13.0%
주민세 3
13.0%
취득세 3
13.0%
자동차세 3
13.0%
등록면허세 3
13.0%
지역자원시설세 3
13.0%
교육세 2
8.7%

Length

2023-12-13T01:11:06.868396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:11:06.986948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
등록세 3
13.0%
재산세 3
13.0%
주민세 3
13.0%
취득세 3
13.0%
자동차세 3
13.0%
등록면허세 3
13.0%
지역자원시설세 3
13.0%
교육세 2
8.7%

과세년도
Categorical

Distinct3
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2018
8 
2019
8 
2017
7 

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 (%)
2018 8
34.8%
2019 8
34.8%
2017 7
30.4%

Length

2023-12-13T01:11:07.123494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:11:07.244769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 8
34.8%
2019 8
34.8%
2017 7
30.4%

비과세금액
Text

Distinct20
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-13T01:11:07.428109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length13
Mean length11.043478
Min length2

Characters and Unicode

Total characters254
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)78.3%

Sample

1st row
2nd row 22,469,073,000
3rd row 47,880,000
4th row 7,781,862,000
5th row 290,400,000
ValueCountFrequency (%)
2
 
10.0%
6,200,000 1
 
5.0%
322,596,000 1
 
5.0%
10,685,000 1
 
5.0%
123,828,000 1
 
5.0%
11,384,901,000 1
 
5.0%
61,340,000 1
 
5.0%
23,231,970,000 1
 
5.0%
478,760,000 1
 
5.0%
6,457,635,000 1
 
5.0%
Other values (9) 9
45.0%
2023-12-13T01:11:07.788393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 68
26.8%
46
18.1%
, 42
16.5%
2 14
 
5.5%
4 12
 
4.7%
3 12
 
4.7%
8 11
 
4.3%
6 11
 
4.3%
1 10
 
3.9%
7 9
 
3.5%
Other values (3) 19
 
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 164
64.6%
Space Separator 46
 
18.1%
Other Punctuation 42
 
16.5%
Dash Punctuation 2
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 68
41.5%
2 14
 
8.5%
4 12
 
7.3%
3 12
 
7.3%
8 11
 
6.7%
6 11
 
6.7%
1 10
 
6.1%
7 9
 
5.5%
5 9
 
5.5%
9 8
 
4.9%
Space Separator
ValueCountFrequency (%)
46
100.0%
Other Punctuation
ValueCountFrequency (%)
, 42
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 254
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 68
26.8%
46
18.1%
, 42
16.5%
2 14
 
5.5%
4 12
 
4.7%
3 12
 
4.7%
8 11
 
4.3%
6 11
 
4.3%
1 10
 
3.9%
7 9
 
3.5%
Other values (3) 19
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 254
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 68
26.8%
46
18.1%
, 42
16.5%
2 14
 
5.5%
4 12
 
4.7%
3 12
 
4.7%
8 11
 
4.3%
6 11
 
4.3%
1 10
 
3.9%
7 9
 
3.5%
Other values (3) 19
 
7.5%

감면금액
Text

Distinct22
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-13T01:11:07.995711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length15
Mean length13.130435
Min length7

Characters and Unicode

Total characters302
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)91.3%

Sample

1st row 38,361,000
2nd row 5,720,699,000
3rd row 46,429,000
4th row 24,786,354,000
5th row 1,196,535,000
ValueCountFrequency (%)
1,000 2
 
8.7%
38,361,000 1
 
4.3%
1,206,677,000 1
 
4.3%
162,498,000 1
 
4.3%
1,393,497,000 1
 
4.3%
24,261,830,000 1
 
4.3%
54,598,000 1
 
4.3%
6,290,174,000 1
 
4.3%
35,750,000 1
 
4.3%
373,404,000 1
 
4.3%
Other values (12) 12
52.2%
2023-12-13T01:11:08.371318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 77
25.5%
, 53
17.5%
46
15.2%
1 18
 
6.0%
3 18
 
6.0%
7 16
 
5.3%
6 15
 
5.0%
4 15
 
5.0%
5 13
 
4.3%
2 12
 
4.0%
Other values (2) 19
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 203
67.2%
Other Punctuation 53
 
17.5%
Space Separator 46
 
15.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 77
37.9%
1 18
 
8.9%
3 18
 
8.9%
7 16
 
7.9%
6 15
 
7.4%
4 15
 
7.4%
5 13
 
6.4%
2 12
 
5.9%
9 12
 
5.9%
8 7
 
3.4%
Other Punctuation
ValueCountFrequency (%)
, 53
100.0%
Space Separator
ValueCountFrequency (%)
46
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 302
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 77
25.5%
, 53
17.5%
46
15.2%
1 18
 
6.0%
3 18
 
6.0%
7 16
 
5.3%
6 15
 
5.0%
4 15
 
5.0%
5 13
 
4.3%
2 12
 
4.0%
Other values (2) 19
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 302
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 77
25.5%
, 53
17.5%
46
15.2%
1 18
 
6.0%
3 18
 
6.0%
7 16
 
5.3%
6 15
 
5.0%
4 15
 
5.0%
5 13
 
4.3%
2 12
 
4.0%
Other values (2) 19
 
6.3%

부과금액
Text

Distinct21
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-13T01:11:08.555231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length16
Mean length14.434783
Min length3

Characters and Unicode

Total characters332
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)87.0%

Sample

1st row -
2nd row 68,224,743,000
3rd row 12,828,152,000
4th row 206,903,246,000
5th row 95,561,382,000
ValueCountFrequency (%)
3
 
13.0%
109,881,400,000 1
 
4.3%
11,531,895,000 1
 
4.3%
64,986,819,000 1
 
4.3%
126,809,374,000 1
 
4.3%
14,147,448,000 1
 
4.3%
78,900,010,000 1
 
4.3%
39,374,319,000 1
 
4.3%
10,384,450,000 1
 
4.3%
10,264,946,000 1
 
4.3%
Other values (11) 11
47.8%
2023-12-13T01:11:08.947984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 77
23.2%
, 60
18.1%
46
13.9%
1 27
 
8.1%
4 20
 
6.0%
8 17
 
5.1%
9 17
 
5.1%
3 16
 
4.8%
6 14
 
4.2%
7 13
 
3.9%
Other values (3) 25
 
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 223
67.2%
Other Punctuation 60
 
18.1%
Space Separator 46
 
13.9%
Dash Punctuation 3
 
0.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 77
34.5%
1 27
 
12.1%
4 20
 
9.0%
8 17
 
7.6%
9 17
 
7.6%
3 16
 
7.2%
6 14
 
6.3%
7 13
 
5.8%
2 12
 
5.4%
5 10
 
4.5%
Other Punctuation
ValueCountFrequency (%)
, 60
100.0%
Space Separator
ValueCountFrequency (%)
46
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 332
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 77
23.2%
, 60
18.1%
46
13.9%
1 27
 
8.1%
4 20
 
6.0%
8 17
 
5.1%
9 17
 
5.1%
3 16
 
4.8%
6 14
 
4.2%
7 13
 
3.9%
Other values (3) 25
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 332
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 77
23.2%
, 60
18.1%
46
13.9%
1 27
 
8.1%
4 20
 
6.0%
8 17
 
5.1%
9 17
 
5.1%
3 16
 
4.8%
6 14
 
4.2%
7 13
 
3.9%
Other values (3) 25
 
7.5%

비과세감면율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)82.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.5491304
Minimum0
Maximum41.32
Zeros5
Zeros (%)21.7%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-13T01:11:09.103774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.75
median1.56
Q312.31
95-th percentile38.5
Maximum41.32
Range41.32
Interquartile range (IQR)11.56

Descriptive statistics

Standard deviation13.927544
Coefficient of variation (CV)1.4585143
Kurtosis0.6255904
Mean9.5491304
Median Absolute Deviation (MAD)1.56
Skewness1.4358564
Sum219.63
Variance193.97647
MonotonicityNot monotonic
2023-12-13T01:11:09.251988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0.0 5
21.7%
41.32 1
 
4.3%
6.13 1
 
4.3%
1.5 1
 
4.3%
2.33 1
 
4.3%
28.11 1
 
4.3%
0.82 1
 
4.3%
37.42 1
 
4.3%
8.21 1
 
4.3%
1.37 1
 
4.3%
Other values (9) 9
39.1%
ValueCountFrequency (%)
0.0 5
21.7%
0.74 1
 
4.3%
0.76 1
 
4.3%
0.82 1
 
4.3%
1.37 1
 
4.3%
1.39 1
 
4.3%
1.5 1
 
4.3%
1.56 1
 
4.3%
1.82 1
 
4.3%
2.33 1
 
4.3%
ValueCountFrequency (%)
41.32 1
4.3%
38.62 1
4.3%
37.42 1
4.3%
28.11 1
4.3%
22.91 1
4.3%
15.74 1
4.3%
8.88 1
4.3%
8.21 1
4.3%
6.13 1
4.3%
2.33 1
4.3%

Interactions

2023-12-13T01:11:05.851279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:11:09.352977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도비과세금액감면금액부과금액비과세감면율
세목명1.0000.0001.0001.0001.0000.752
과세년도0.0001.0000.0000.7360.0000.000
비과세금액1.0000.0001.0001.0001.0001.000
감면금액1.0000.7361.0001.0000.9531.000
부과금액1.0000.0001.0000.9531.0001.000
비과세감면율0.7520.0001.0001.0001.0001.000
2023-12-13T01:11:09.499273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명
과세년도1.0000.000
세목명0.0001.000
2023-12-13T01:11:09.609979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비과세감면율세목명과세년도
비과세감면율1.0000.5000.000
세목명0.5001.0000.000
과세년도0.0000.0001.000

Missing values

2023-12-13T01:11:06.170985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:11:06.271709image/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
0경상남도양산시48330등록세201738,361,000-0.0
1경상남도양산시48330재산세201722,469,073,0005,720,699,00068,224,743,00041.32
2경상남도양산시48330주민세201747,880,00046,429,00012,828,152,0000.74
3경상남도양산시48330취득세20177,781,862,00024,786,354,000206,903,246,00015.74
4경상남도양산시48330자동차세2017290,400,0001,196,535,00095,561,382,0001.56
5경상남도양산시48330등록면허세20173,578,000243,190,00013,584,398,0001.82
6경상남도양산시48330지역자원시설세2017456,353,000357,439,0009,165,519,0008.88
7경상남도양산시48330교육세2018-1,00039,364,030,0000.0
8경상남도양산시48330등록세201821,251,000-0.0
9경상남도양산시48330재산세201822,493,465,0006,166,309,00074,216,676,00038.62
시도명시군구명자치단체코드세목명과세년도비과세금액감면금액부과금액비과세감면율
13경상남도양산시48330등록면허세20186,200,000134,436,00010,264,946,0001.37
14경상남도양산시48330지역자원시설세2018478,760,000373,404,00010,384,450,0008.21
15경상남도양산시48330교육세2019-1,00039,374,319,0000.0
16경상남도양산시48330등록세201935,750,000-0.0
17경상남도양산시48330재산세201923,231,970,0006,290,174,00078,900,010,00037.42
18경상남도양산시48330주민세201961,340,00054,598,00014,147,448,0000.82
19경상남도양산시48330취득세201911,384,901,00024,261,830,000126,809,374,00028.11
20경상남도양산시48330자동차세2019123,828,0001,393,497,00064,986,819,0002.33
21경상남도양산시48330등록면허세201910,685,000162,498,00011,531,895,0001.5
22경상남도양산시48330지역자원시설세2019508,219,000177,185,00011,185,777,0006.13