Overview

Dataset statistics

Number of variables11
Number of observations26
Missing cells34
Missing cells (%)11.9%
Duplicate rows1
Duplicate rows (%)3.8%
Total size in memory2.4 KiB
Average record size in memory92.9 B

Variable types

Text1
Unsupported10

Dataset

Description2023년 지방세 통계연감(2022년 기준) 자료 중 지방자치단체별 지방소득세(소득세분, 법인세분, 특별징수) 부과건수, 부과세액 등 과세 현황
Author행정안전부
URLhttps://www.data.go.kr/data/15038887/fileData.do

Alerts

Dataset has 1 (3.8%) duplicate rowsDuplicates
8-3. 시도별 지방소득세 과세상황 has 2 (7.7%) missing valuesMissing
Unnamed: 1 has 3 (11.5%) missing valuesMissing
Unnamed: 2 has 4 (15.4%) missing valuesMissing
Unnamed: 3 has 2 (7.7%) missing valuesMissing
Unnamed: 4 has 3 (11.5%) missing valuesMissing
Unnamed: 5 has 3 (11.5%) missing valuesMissing
Unnamed: 6 has 4 (15.4%) missing valuesMissing
Unnamed: 7 has 2 (7.7%) missing valuesMissing
Unnamed: 8 has 4 (15.4%) missing valuesMissing
Unnamed: 9 has 3 (11.5%) missing valuesMissing
Unnamed: 10 has 4 (15.4%) missing valuesMissing
Unnamed: 1 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 2 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 3 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 4 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 5 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 6 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 7 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 8 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 9 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 10 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-04-13 12:53:59.475131
Analysis finished2024-04-13 12:54:01.665773
Duration2.19 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

8-3. 시도별 지방소득세 과세상황
Text

MISSING 

Distinct24
Distinct (%)100.0%
Missing2
Missing (%)7.7%
Memory size336.0 B
2024-04-13T21:54:02.064512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length15
Mean length5.5
Min length2

Characters and Unicode

Total characters132
Distinct characters47
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)100.0%

Sample

1st row회계년월 : 2022년 결산
2nd row건수 : 건, 세액 : 천원
3rd row구분
4th row합 계
5th row시 계
ValueCountFrequency (%)
계 4
 
11.1%
3
 
8.3%
광주광역시 1
 
2.8%
회계년월 1
 
2.8%
대전광역시 1
 
2.8%
경상남도 1
 
2.8%
경상북도 1
 
2.8%
전라남도 1
 
2.8%
전라북도 1
 
2.8%
충청남도 1
 
2.8%
Other values (21) 21
58.3%
2024-04-13T21:54:03.294652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
21
 
15.9%
시 9
 
6.8%
도 9
 
6.8%
광 7
 
5.3%
역 6
 
4.5%
계 5
 
3.8%
별 3
 
2.3%
2 3
 
2.3%
: 3
 
2.3%
경 3
 
2.3%
Other values (37) 63
47.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 103
78.0%
Space Separator 21
 
15.9%
Decimal Number 4
 
3.0%
Other Punctuation 4
 
3.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 9
 
8.7%
도 9
 
8.7%
광 7
 
6.8%
역 6
 
5.8%
계 5
 
4.9%
별 3
 
2.9%
경 3
 
2.9%
산 3
 
2.9%
특 3
 
2.9%
북 3
 
2.9%
Other values (32) 52
50.5%
Decimal Number
ValueCountFrequency (%)
2 3
75.0%
0 1
 
25.0%
Other Punctuation
ValueCountFrequency (%)
: 3
75.0%
, 1
 
25.0%
Space Separator
ValueCountFrequency (%)
21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 103
78.0%
Common 29
 
22.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 9
 
8.7%
도 9
 
8.7%
광 7
 
6.8%
역 6
 
5.8%
계 5
 
4.9%
별 3
 
2.9%
경 3
 
2.9%
산 3
 
2.9%
특 3
 
2.9%
북 3
 
2.9%
Other values (32) 52
50.5%
Common
ValueCountFrequency (%)
21
72.4%
2 3
 
10.3%
: 3
 
10.3%
0 1
 
3.4%
, 1
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 103
78.0%
ASCII 29
 
22.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21
72.4%
2 3
 
10.3%
: 3
 
10.3%
0 1
 
3.4%
, 1
 
3.4%
Hangul
ValueCountFrequency (%)
시 9
 
8.7%
도 9
 
8.7%
광 7
 
6.8%
역 6
 
5.8%
계 5
 
4.9%
별 3
 
2.9%
경 3
 
2.9%
산 3
 
2.9%
특 3
 
2.9%
북 3
 
2.9%
Other values (32) 52
50.5%

Unnamed: 1
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3
Missing (%)11.5%
Memory size336.0 B

Unnamed: 2
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4
Missing (%)15.4%
Memory size336.0 B

Unnamed: 3
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2
Missing (%)7.7%
Memory size336.0 B

Unnamed: 4
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3
Missing (%)11.5%
Memory size336.0 B

Unnamed: 5
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3
Missing (%)11.5%
Memory size336.0 B

Unnamed: 6
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4
Missing (%)15.4%
Memory size336.0 B

Unnamed: 7
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2
Missing (%)7.7%
Memory size336.0 B

Unnamed: 8
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4
Missing (%)15.4%
Memory size336.0 B

Unnamed: 9
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3
Missing (%)11.5%
Memory size336.0 B

Unnamed: 10
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4
Missing (%)15.4%
Memory size336.0 B

Missing values

2024-04-13T21:54:00.531200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-13T21:54:01.079508image/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.
2024-04-13T21:54:01.429214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

8-3. 시도별 지방소득세 과세상황Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10
0회계년월 : 2022년 결산NaNNaNNaN시도선택 : 전체(시도별)NaNNaN비자치구처리 : 구청합산NaNNaNNaN
1건수 : 건, 세액 : 천원NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2구분합계NaN소득분NaNNaNNaNNaNNaN특별징수NaN
3<NA>NaNNaN소계NaN소득세분NaN법인세분NaNNaNNaN
4<NA>건수세액건수세액건수세액건수세액건수세액
5합 계2312909025005001321124805061642702798411655883615040652282462310276621462106485848577973337
6시 계12973715107995352437063492748023797566213002430790881442192504944709459102233319297268
7군 계1740441112577671785321473530476075966424052893693550494775824887227390471957
8구 계8414934130796893614563800821148524942749193479086705288881473239854438511344868204112
9서울특별시269807990069112201393810549335208412678252381016156125985311233592813042693513559136
8-3. 시도별 지방소득세 과세상황Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10
16세종특별자치시160695116805559882857723352682799318736815486453598457241039572033
17경기도801290066555397654328701469876370940784141676432648250287302233106136841991956776056
18강원도65745535949240335068823292506932420210987219126486123052878306767126567334
19충청북도71017868843924538229649161480235025110182144532045389793357327882196824443
20충청남도99513093007208152287762488384248248014479672440397480087118472253305188239
21전라북도6680563957521763762122577976753454919154335130721166254324291844137954501
22전라남도6758386535304063553594508183363171509017746938209360640867320479202712070
23경상북도101085991694943453221858751279748749313350346644725454009331478641329436637
24경상남도133929180940294672128449595659067261818699276148666308963829618007313446356
25제주특별자치도30729521874370417097316550321715949884391419114758111179813632253240487

Duplicate rows

Most frequently occurring

8-3. 시도별 지방소득세 과세상황# duplicates
0<NA>2