Overview

Dataset statistics

Number of variables9
Number of observations39
Missing cells16
Missing cells (%)4.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.1 KiB
Average record size in memory81.3 B

Variable types

Categorical5
Numeric4

Dataset

Description3개년간(2020~2022) 연도별 지방세 과세 및 비과세 현황을 세목별로 제공하는 데이터로 과세건수, 과세금액, 비과세건수, 비과세금액 등의 항목을 제공합니다.
Author전라남도 나주시
URLhttps://www.data.go.kr/data/15126697/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
과세건수 is highly overall correlated with 비과세건수 and 1 other fieldsHigh correlation
과세금액 is highly overall correlated with 세목명High correlation
비과세건수 is highly overall correlated with 과세건수 and 2 other fieldsHigh correlation
비과세금액 is highly overall correlated with 비과세건수High correlation
세목명 is highly overall correlated with 과세건수 and 2 other fieldsHigh correlation
과세건수 has 4 (10.3%) missing valuesMissing
과세금액 has 4 (10.3%) missing valuesMissing
비과세건수 has 4 (10.3%) missing valuesMissing
비과세금액 has 4 (10.3%) missing valuesMissing
과세건수 has 4 (10.3%) zerosZeros
과세금액 has 4 (10.3%) zerosZeros
비과세건수 has 11 (28.2%) zerosZeros
비과세금액 has 11 (28.2%) zerosZeros

Reproduction

Analysis started2024-03-14 18:17:22.384201
Analysis finished2024-03-14 18:17:26.926960
Duration4.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size440.0 B
전라남도
39 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전라남도 39
100.0%

Length

2024-03-15T03:17:27.196890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T03:17:27.503037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라남도 39
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size440.0 B
나주시
39 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
나주시 39
100.0%

Length

2024-03-15T03:17:27.832227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T03:17:28.078603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
나주시 39
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size440.0 B
46170
39 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row46170
2nd row46170
3rd row46170
4th row46170
5th row46170

Common Values

ValueCountFrequency (%)
46170 39
100.0%

Length

2024-03-15T03:17:28.401531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T03:17:28.708615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
46170 39
100.0%

과세년도
Categorical

Distinct3
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size440.0 B
2020
13 
2021
13 
2022
13 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 13
33.3%
2021 13
33.3%
2022 13
33.3%

Length

2024-03-15T03:17:29.111813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T03:17:29.496323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 13
33.3%
2021 13
33.3%
2022 13
33.3%

세목명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size440.0 B
취득세
3 
등록세
3 
주민세
3 
재산세
3 
자동차세
3 
Other values (8)
24 

Length

Max length7
Median length5
Mean length4.1538462
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
취득세 3
 
7.7%
등록세 3
 
7.7%
주민세 3
 
7.7%
재산세 3
 
7.7%
자동차세 3
 
7.7%
레저세 3
 
7.7%
담배소비세 3
 
7.7%
지방소비세 3
 
7.7%
등록면허세 3
 
7.7%
도시계획세 3
 
7.7%
Other values (3) 9
23.1%

Length

2024-03-15T03:17:29.737821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
취득세 3
 
7.7%
등록세 3
 
7.7%
주민세 3
 
7.7%
재산세 3
 
7.7%
자동차세 3
 
7.7%
레저세 3
 
7.7%
담배소비세 3
 
7.7%
지방소비세 3
 
7.7%
등록면허세 3
 
7.7%
도시계획세 3
 
7.7%
Other values (3) 9
23.1%

과세건수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct32
Distinct (%)91.4%
Missing4
Missing (%)10.3%
Infinite0
Infinite (%)0.0%
Mean72418.686
Minimum0
Maximum327549
Zeros4
Zeros (%)10.3%
Negative0
Negative (%)0.0%
Memory size479.0 B
2024-03-15T03:17:29.967335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1375.5
median52474
Q386180
95-th percentile318942.6
Maximum327549
Range327549
Interquartile range (IQR)85804.5

Descriptive statistics

Standard deviation91038.746
Coefficient of variation (CV)1.2571168
Kurtosis3.0506532
Mean72418.686
Median Absolute Deviation (MAD)50083
Skewness1.8593612
Sum2534654
Variance8.2880532 × 109
MonotonicityNot monotonic
2024-03-15T03:17:30.265720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 4
 
10.3%
326385 1
 
2.6%
52474 1
 
2.6%
53728 1
 
2.6%
69616 1
 
2.6%
9 1
 
2.6%
639 1
 
2.6%
73734 1
 
2.6%
102557 1
 
2.6%
163946 1
 
2.6%
Other values (22) 22
56.4%
(Missing) 4
 
10.3%
ValueCountFrequency (%)
0 4
10.3%
6 1
 
2.6%
7 1
 
2.6%
9 1
 
2.6%
45 1
 
2.6%
274 1
 
2.6%
477 1
 
2.6%
639 1
 
2.6%
26381 1
 
2.6%
26410 1
 
2.6%
ValueCountFrequency (%)
327549 1
2.6%
326385 1
2.6%
315753 1
2.6%
163946 1
2.6%
160087 1
2.6%
156404 1
2.6%
102557 1
2.6%
102470 1
2.6%
98626 1
2.6%
73734 1
2.6%

과세금액
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct32
Distinct (%)91.4%
Missing4
Missing (%)10.3%
Infinite0
Infinite (%)0.0%
Mean1.7401991 × 1010
Minimum0
Maximum5.9486155 × 1010
Zeros4
Zeros (%)10.3%
Negative0
Negative (%)0.0%
Memory size479.0 B
2024-03-15T03:17:30.707434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.9530875 × 109
median1.0067081 × 1010
Q32.6659636 × 1010
95-th percentile5.3587786 × 1010
Maximum5.9486155 × 1010
Range5.9486155 × 1010
Interquartile range (IQR)2.1706548 × 1010

Descriptive statistics

Standard deviation1.7334787 × 1010
Coefficient of variation (CV)0.99613811
Kurtosis0.43473392
Mean1.7401991 × 1010
Median Absolute Deviation (MAD)5.864542 × 109
Skewness1.1747656
Sum6.0906969 × 1011
Variance3.0049483 × 1020
MonotonicityNot monotonic
2024-03-15T03:17:31.117871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 4
 
10.3%
15931623000 1
 
2.6%
51583831000 1
 
2.6%
5302137000 1
 
2.6%
4596184000 1
 
2.6%
14878229000 1
 
2.6%
8428606000 1
 
2.6%
4883519000 1
 
2.6%
23935783000 1
 
2.6%
31773897000 1
 
2.6%
Other values (22) 22
56.4%
(Missing) 4
 
10.3%
ValueCountFrequency (%)
0 4
10.3%
62881000 1
 
2.6%
4516876000 1
 
2.6%
4596184000 1
 
2.6%
4883519000 1
 
2.6%
4943197000 1
 
2.6%
4962978000 1
 
2.6%
5302137000 1
 
2.6%
6331017000 1
 
2.6%
7232592000 1
 
2.6%
ValueCountFrequency (%)
59486155000 1
2.6%
58263680000 1
2.6%
51583831000 1
2.6%
50954808000 1
2.6%
36877328000 1
2.6%
35833210000 1
2.6%
31773897000 1
2.6%
29623689000 1
2.6%
27565251000 1
2.6%
25754021000 1
2.6%

비과세건수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct25
Distinct (%)71.4%
Missing4
Missing (%)10.3%
Infinite0
Infinite (%)0.0%
Mean6721.0571
Minimum0
Maximum41406
Zeros11
Zeros (%)28.2%
Negative0
Negative (%)0.0%
Memory size479.0 B
2024-03-15T03:17:31.360923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2817
Q36931
95-th percentile39749.9
Maximum41406
Range41406
Interquartile range (IQR)6931

Descriptive statistics

Standard deviation11479.33
Coefficient of variation (CV)1.7079649
Kurtosis4.619821
Mean6721.0571
Median Absolute Deviation (MAD)2817
Skewness2.2978715
Sum235237
Variance1.3177501 × 108
MonotonicityNot monotonic
2024-03-15T03:17:31.600542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 11
28.2%
5909 1
 
2.6%
168 1
 
2.6%
2850 1
 
2.6%
6179 1
 
2.6%
16228 1
 
2.6%
41406 1
 
2.6%
9483 1
 
2.6%
7 1
 
2.6%
4637 1
 
2.6%
Other values (15) 15
38.5%
(Missing) 4
 
10.3%
ValueCountFrequency (%)
0 11
28.2%
7 1
 
2.6%
24 1
 
2.6%
44 1
 
2.6%
112 1
 
2.6%
125 1
 
2.6%
168 1
 
2.6%
2817 1
 
2.6%
2850 1
 
2.6%
2874 1
 
2.6%
ValueCountFrequency (%)
41406 1
2.6%
40074 1
2.6%
39611 1
2.6%
16228 1
2.6%
16144 1
2.6%
15124 1
2.6%
9483 1
2.6%
8696 1
2.6%
7683 1
2.6%
6179 1
2.6%

비과세금액
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct25
Distinct (%)71.4%
Missing4
Missing (%)10.3%
Infinite0
Infinite (%)0.0%
Mean2.6737703 × 109
Minimum0
Maximum2.4961374 × 1010
Zeros11
Zeros (%)28.2%
Negative0
Negative (%)0.0%
Memory size479.0 B
2024-03-15T03:17:31.964455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median23544000
Q35.89838 × 108
95-th percentile1.420967 × 1010
Maximum2.4961374 × 1010
Range2.4961374 × 1010
Interquartile range (IQR)5.89838 × 108

Descriptive statistics

Standard deviation5.9880661 × 109
Coefficient of variation (CV)2.2395589
Kurtosis5.2514429
Mean2.6737703 × 109
Median Absolute Deviation (MAD)23544000
Skewness2.3879197
Sum9.3581962 × 1010
Variance3.5856936 × 1019
MonotonicityNot monotonic
2024-03-15T03:17:32.374639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 11
28.2%
296460000 1
 
2.6%
28000 1
 
2.6%
501221000 1
 
2.6%
223245000 1
 
2.6%
678455000 1
 
2.6%
14938938000 1
 
2.6%
24609000 1
 
2.6%
320000 1
 
2.6%
8844548000 1
 
2.6%
Other values (15) 15
38.5%
(Missing) 4
 
10.3%
ValueCountFrequency (%)
0 11
28.2%
9000 1
 
2.6%
10000 1
 
2.6%
28000 1
 
2.6%
320000 1
 
2.6%
1629000 1
 
2.6%
18473000 1
 
2.6%
23544000 1
 
2.6%
24607000 1
 
2.6%
24609000 1
 
2.6%
ValueCountFrequency (%)
24961374000 1
2.6%
14938938000 1
2.6%
13897126000 1
2.6%
13512634000 1
2.6%
12913074000 1
2.6%
8844548000 1
2.6%
706226000 1
2.6%
693751000 1
2.6%
678455000 1
2.6%
501221000 1
2.6%

Interactions

2024-03-15T03:17:25.281919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:17:22.708134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:17:23.710422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:17:24.404457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:17:25.540431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:17:22.955302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:17:23.958997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:17:24.565087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:17:25.741942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:17:23.210214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:17:24.111840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:17:24.814698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:17:25.961024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:17:23.457260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:17:24.247540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:17:25.041894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T03:17:32.597117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명과세건수과세금액비과세건수비과세금액
과세년도1.0000.0000.0000.0000.0000.000
세목명0.0001.0000.9600.8680.9620.696
과세건수0.0000.9601.0000.8300.8430.487
과세금액0.0000.8680.8301.0000.7420.769
비과세건수0.0000.9620.8430.7421.0000.575
비과세금액0.0000.6960.4870.7690.5751.000
2024-03-15T03:17:32.786385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명
과세년도1.0000.000
세목명0.0001.000
2024-03-15T03:17:32.996172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세건수과세금액비과세건수비과세금액과세년도세목명
과세건수1.0000.3670.6980.4930.0000.764
과세금액0.3671.0000.1880.2630.0000.561
비과세건수0.6980.1881.0000.8860.0000.782
비과세금액0.4930.2630.8861.0000.0000.402
과세년도0.0000.0000.0000.0001.0000.000
세목명0.7640.5610.7820.4020.0001.000

Missing values

2024-03-15T03:17:26.159693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T03:17:26.427131image/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.
2024-03-15T03:17:26.762822image/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

시도명시군구명자치단체코드과세년도세목명과세건수과세금액비과세건수비과세금액
0전라남도나주시461702020취득세2638150954808000464312913074000
1전라남도나주시461702020등록세00241629000
2전라남도나주시461702020주민세622886331017000768323544000
3전라남도나주시461702020재산세156404257540210003961113512634000
4전라남도나주시461702020자동차세986262217077300015124706226000
5전라남도나주시461702020레저세<NA><NA><NA><NA>
6전라남도나주시461702020담배소비세274787813400000
7전라남도나주시461702020지방소비세61015830000000
8전라남도나주시461702020등록면허세7060849629780005409405563000
9전라남도나주시461702020도시계획세<NA><NA><NA><NA>
시도명시군구명자치단체코드과세년도세목명과세건수과세금액비과세건수비과세금액
29전라남도나주시461702022재산세163946317738970004140614938938000
30전라남도나주시461702022자동차세1025572393578300016228678455000
31전라남도나주시461702022레저세456288100000
32전라남도나주시461702022담배소비세639842860600000
33전라남도나주시461702022지방소비세91487822900000
34전라남도나주시461702022등록면허세6961645961840006179223245000
35전라남도나주시461702022도시계획세0000
36전라남도나주시461702022지역자원시설세5372853021370002850501221000
37전라남도나주시461702022지방소득세524745158383100000
38전라남도나주시461702022교육세3263851593162300016828000