Overview

Dataset statistics

Number of variables9
Number of observations46
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory78.9 B

Variable types

Categorical6
Text1
Numeric2

Dataset

Description충청북도 증평군_지방세에 대한 자료입니다. 지방세에는 취득세, 재산세, 자동차세, 지방소득세, 등록면허세 등 다양한 자료가 있습니다.
URLhttps://www.data.go.kr/data/15080367/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
과세년도 has constant value ""Constant
데이터기준일자 has constant value ""Constant
부과건수 is highly overall correlated with 부과금액 and 1 other fieldsHigh correlation
부과금액 is highly overall correlated with 부과건수High correlation
세목명 is highly overall correlated with 부과건수High correlation
세원 유형명 has unique valuesUnique
부과건수 has 11 (23.9%) zerosZeros
부과금액 has 11 (23.9%) zerosZeros

Reproduction

Analysis started2023-12-12 23:45:50.152253
Analysis finished2023-12-12 23:45:50.925247
Duration0.77 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size500.0 B
충청북도
46 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
충청북도 46
100.0%

Length

2023-12-13T08:45:50.980791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:45:51.080656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청북도 46
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size500.0 B
증평군
46 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
증평군 46
100.0%

Length

2023-12-13T08:45:51.175243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:45:51.266143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
증평군 46
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size500.0 B
43745
46 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row43745
2nd row43745
3rd row43745
4th row43745
5th row43745

Common Values

ValueCountFrequency (%)
43745 46
100.0%

Length

2023-12-13T08:45:51.347434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:45:51.423918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
43745 46
100.0%

과세년도
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size500.0 B
2022
46 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 46
100.0%

Length

2023-12-13T08:45:51.522007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:45:51.602988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 46
100.0%

세목명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)28.3%
Missing0
Missing (%)0.0%
Memory size500.0 B
취득세
9 
주민세
7 
자동차세
7 
재산세
5 
지방소득세
4 
Other values (8)
14 

Length

Max length7
Median length3
Mean length3.7826087
Min length2

Unique

Unique5 ?
Unique (%)10.9%

Sample

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

Common Values

ValueCountFrequency (%)
취득세 9
19.6%
주민세 7
15.2%
자동차세 7
15.2%
재산세 5
10.9%
지방소득세 4
8.7%
레저세 4
8.7%
지역자원시설세 3
 
6.5%
등록면허세 2
 
4.3%
교육세 1
 
2.2%
도시계획세 1
 
2.2%
Other values (3) 3
 
6.5%

Length

2023-12-13T08:45:51.687269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
취득세 9
19.6%
주민세 7
15.2%
자동차세 7
15.2%
재산세 5
10.9%
지방소득세 4
8.7%
레저세 4
8.7%
지역자원시설세 3
 
6.5%
등록면허세 2
 
4.3%
교육세 1
 
2.2%
도시계획세 1
 
2.2%
Other values (3) 3
 
6.5%

세원 유형명
Text

UNIQUE 

Distinct46
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size500.0 B
2023-12-13T08:45:51.894602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length8
Mean length6.0217391
Min length2

Characters and Unicode

Total characters277
Distinct characters73
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

Unique46 ?
Unique (%)100.0%

Sample

1st row교육세
2nd row도시계획세
3rd row건축물
4th row주택(개별)
5th row주택(단독)
ValueCountFrequency (%)
교육세 1
 
2.2%
특수 1
 
2.2%
지역자원시설세(특자 1
 
2.2%
지방소득세(양도소득 1
 
2.2%
지방소득세(종합소득 1
 
2.2%
소싸움 1
 
2.2%
경정 1
 
2.2%
경륜 1
 
2.2%
경마 1
 
2.2%
자동차세(주행 1
 
2.2%
Other values (36) 36
78.3%
2023-12-13T08:45:52.224522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
세 27
 
9.7%
( 24
 
8.7%
) 24
 
8.7%
소 14
 
5.1%
주 11
 
4.0%
지 10
 
3.6%
득 9
 
3.2%
민 7
 
2.5%
방 6
 
2.2%
시 5
 
1.8%
Other values (63) 140
50.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 228
82.3%
Open Punctuation 24
 
8.7%
Close Punctuation 24
 
8.7%
Decimal Number 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
세 27
 
11.8%
소 14
 
6.1%
주 11
 
4.8%
지 10
 
4.4%
득 9
 
3.9%
민 7
 
3.1%
방 6
 
2.6%
시 5
 
2.2%
자 5
 
2.2%
산 5
 
2.2%
Other values (60) 129
56.6%
Open Punctuation
ValueCountFrequency (%)
( 24
100.0%
Close Punctuation
ValueCountFrequency (%)
) 24
100.0%
Decimal Number
ValueCountFrequency (%)
3 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 228
82.3%
Common 49
 
17.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
세 27
 
11.8%
소 14
 
6.1%
주 11
 
4.8%
지 10
 
4.4%
득 9
 
3.9%
민 7
 
3.1%
방 6
 
2.6%
시 5
 
2.2%
자 5
 
2.2%
산 5
 
2.2%
Other values (60) 129
56.6%
Common
ValueCountFrequency (%)
( 24
49.0%
) 24
49.0%
3 1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 228
82.3%
ASCII 49
 
17.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
세 27
 
11.8%
소 14
 
6.1%
주 11
 
4.8%
지 10
 
4.4%
득 9
 
3.9%
민 7
 
3.1%
방 6
 
2.6%
시 5
 
2.2%
자 5
 
2.2%
산 5
 
2.2%
Other values (60) 129
56.6%
ASCII
ValueCountFrequency (%)
( 24
49.0%
) 24
49.0%
3 1
 
2.0%

부과건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)78.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4849.6739
Minimum0
Maximum75680
Zeros11
Zeros (%)23.9%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-13T08:45:52.373222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19.5
median346.5
Q33866.25
95-th percentile20775.25
Maximum75680
Range75680
Interquartile range (IQR)3856.75

Descriptive statistics

Standard deviation12174.839
Coefficient of variation (CV)2.5104448
Kurtosis26.172844
Mean4849.6739
Median Absolute Deviation (MAD)346.5
Skewness4.7217936
Sum223085
Variance1.4822669 × 108
MonotonicityNot monotonic
2023-12-13T08:45:52.493352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 11
23.9%
124 1
 
2.2%
11 1
 
2.2%
34 1
 
2.2%
12 1
 
2.2%
225 1
 
2.2%
159 1
 
2.2%
4105 1
 
2.2%
688 1
 
2.2%
75680 1
 
2.2%
Other values (26) 26
56.5%
ValueCountFrequency (%)
0 11
23.9%
9 1
 
2.2%
11 1
 
2.2%
12 1
 
2.2%
16 1
 
2.2%
34 1
 
2.2%
77 1
 
2.2%
110 1
 
2.2%
124 1
 
2.2%
159 1
 
2.2%
ValueCountFrequency (%)
75680 1
2.2%
23406 1
2.2%
22387 1
2.2%
15940 1
2.2%
14212 1
2.2%
11347 1
2.2%
10612 1
2.2%
9459 1
2.2%
7421 1
2.2%
7276 1
2.2%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)78.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4489278 × 109
Minimum0
Maximum1.0727733 × 1010
Zeros11
Zeros (%)23.9%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-13T08:45:52.622078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11003000
median2.496825 × 108
Q32.2993718 × 109
95-th percentile5.1510745 × 109
Maximum1.0727733 × 1010
Range1.0727733 × 1010
Interquartile range (IQR)2.2983688 × 109

Descriptive statistics

Standard deviation2.3884955 × 109
Coefficient of variation (CV)1.6484572
Kurtosis7.0819305
Mean1.4489278 × 109
Median Absolute Deviation (MAD)2.496825 × 108
Skewness2.5208027
Sum6.6650679 × 1010
Variance5.7049109 × 1018
MonotonicityNot monotonic
2023-12-13T08:45:52.778277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 11
23.9%
8627000 1
 
2.2%
5540000 1
 
2.2%
13971000 1
 
2.2%
2377620000 1
 
2.2%
2590000 1
 
2.2%
6453000 1
 
2.2%
101041000 1
 
2.2%
36985000 1
 
2.2%
4512988000 1
 
2.2%
Other values (26) 26
56.5%
ValueCountFrequency (%)
0 11
23.9%
474000 1
 
2.2%
2590000 1
 
2.2%
3523000 1
 
2.2%
5540000 1
 
2.2%
6453000 1
 
2.2%
8627000 1
 
2.2%
13971000 1
 
2.2%
36985000 1
 
2.2%
54413000 1
 
2.2%
ValueCountFrequency (%)
10727733000 1
2.2%
9969533000 1
2.2%
5363770000 1
2.2%
4512988000 1
2.2%
3978649000 1
2.2%
3426115000 1
2.2%
3261300000 1
2.2%
3156374000 1
2.2%
3147934000 1
2.2%
2441279000 1
2.2%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size500.0 B
2022-12-31
46 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-12-31
2nd row2022-12-31
3rd row2022-12-31
4th row2022-12-31
5th row2022-12-31

Common Values

ValueCountFrequency (%)
2022-12-31 46
100.0%

Length

2023-12-13T08:45:52.894623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:45:52.968440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-12-31 46
100.0%

Interactions

2023-12-13T08:45:50.539129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:45:50.344870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:45:50.635496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:45:50.440051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:45:53.019636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명세원 유형명부과건수부과금액
세목명1.0001.0000.8570.678
세원 유형명1.0001.0001.0001.000
부과건수0.8571.0001.0000.516
부과금액0.6781.0000.5161.000
2023-12-13T08:45:53.115889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과건수부과금액세목명
부과건수1.0000.7170.615
부과금액0.7171.0000.373
세목명0.6150.3731.000

Missing values

2023-12-13T08:45:50.734227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:45:50.864365image/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 유형명부과건수부과금액데이터기준일자
0충청북도증평군437452022교육세교육세7568045129880002022-12-31
1충청북도증평군437452022도시계획세도시계획세002022-12-31
2충청북도증평군437452022취득세건축물40123412510002022-12-31
3충청북도증평군437452022취득세주택(개별)32013458870002022-12-31
4충청북도증평군437452022취득세주택(단독)151453637700002022-12-31
5충청북도증평군437452022취득세기타1107021660002022-12-31
6충청북도증평군437452022취득세항공기002022-12-31
7충청북도증평군437452022취득세기계장비77544130002022-12-31
8충청북도증평군437452022취득세차량315031479340002022-12-31
9충청북도증평군437452022취득세선박002022-12-31
시도명시군구명자치단체코드과세년도세목명세원 유형명부과건수부과금액데이터기준일자
36충청북도증평군437452022자동차세기타승용12486270002022-12-31
37충청북도증평군437452022자동차세승용2238734261150002022-12-31
38충청북도증평군437452022담배소비세담배소비세63639786490002022-12-31
39충청북도증평군437452022지방소비세지방소비세999695330002022-12-31
40충청북도증평군437452022등록면허세등록면허세(면허)7421826110002022-12-31
41충청북도증평군437452022등록면허세등록면허세(등록)94597996760002022-12-31
42충청북도증평군437452022지역자원시설세지역자원시설세(소방)1061211760190002022-12-31
43충청북도증평군437452022지역자원시설세지역자원시설세(시설)002022-12-31
44충청북도증평군437452022지역자원시설세지역자원시설세(특자)24735230002022-12-31
45충청북도증평군437452022체납체납2340621737340002022-12-31