Overview

Dataset statistics

Number of variables11
Number of observations39
Missing cells3
Missing cells (%)0.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 KiB
Average record size in memory98.4 B

Variable types

Numeric5
Categorical5
DateTime1

Dataset

Description평창군 지방세 1인당 부담액에 대한 데이터로, 과세년도, 비과세금액, 감면금액, 부과금액, 비과세감면율을 제공합니다.(2017~2021)
Author강원도 평창군
URLhttps://www.data.go.kr/data/15080519/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
데이터기준일자 has constant value ""Constant
순번 is highly overall correlated with 과세년도High correlation
비과세금액 is highly overall correlated with 감면금액 and 3 other fieldsHigh correlation
감면금액 is highly overall correlated with 비과세금액 and 2 other fieldsHigh correlation
부과금액 is highly overall correlated with 비과세금액 and 3 other fieldsHigh correlation
비과세감면율 is highly overall correlated with 비과세금액 and 3 other fieldsHigh correlation
세목명 is highly overall correlated with 비과세금액 and 2 other fieldsHigh correlation
과세년도 is highly overall correlated with 순번High correlation
비과세금액 has 3 (7.7%) missing valuesMissing
순번 has unique valuesUnique
감면금액 has unique valuesUnique
비과세금액 has 6 (15.4%) zerosZeros
부과금액 has 3 (7.7%) zerosZeros
비과세감면율 has 8 (20.5%) zerosZeros

Reproduction

Analysis started2023-12-12 21:36:22.120050
Analysis finished2023-12-12 21:36:25.039538
Duration2.92 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct39
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20
Minimum1
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T06:36:25.119623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.9
Q110.5
median20
Q329.5
95-th percentile37.1
Maximum39
Range38
Interquartile range (IQR)19

Descriptive statistics

Standard deviation11.401754
Coefficient of variation (CV)0.57008771
Kurtosis-1.2
Mean20
Median Absolute Deviation (MAD)10
Skewness0
Sum780
Variance130
MonotonicityStrictly increasing
2023-12-13T06:36:25.345155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
1 1
 
2.6%
2 1
 
2.6%
23 1
 
2.6%
24 1
 
2.6%
25 1
 
2.6%
26 1
 
2.6%
27 1
 
2.6%
28 1
 
2.6%
29 1
 
2.6%
30 1
 
2.6%
Other values (29) 29
74.4%
ValueCountFrequency (%)
1 1
2.6%
2 1
2.6%
3 1
2.6%
4 1
2.6%
5 1
2.6%
6 1
2.6%
7 1
2.6%
8 1
2.6%
9 1
2.6%
10 1
2.6%
ValueCountFrequency (%)
39 1
2.6%
38 1
2.6%
37 1
2.6%
36 1
2.6%
35 1
2.6%
34 1
2.6%
33 1
2.6%
32 1
2.6%
31 1
2.6%
30 1
2.6%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size444.0 B
강원도
39 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
강원도 39
100.0%

Length

2023-12-13T06:36:25.666128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:36:25.917238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강원도 39
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size444.0 B
평창군
39 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
평창군 39
100.0%

Length

2023-12-13T06:36:26.154691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:36:26.419482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
평창군 39
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size444.0 B
42760
39 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row42760
2nd row42760
3rd row42760
4th row42760
5th row42760

Common Values

ValueCountFrequency (%)
42760 39
100.0%

Length

2023-12-13T06:36:26.684285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:36:26.895516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
42760 39
100.0%

세목명
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)20.5%
Missing0
Missing (%)0.0%
Memory size444.0 B
교육세
5 
재산세
5 
주민세
5 
취득세
5 
자동차세
5 
Other values (3)
14 

Length

Max length7
Median length3
Mean length3.8974359
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
교육세 5
12.8%
재산세 5
12.8%
주민세 5
12.8%
취득세 5
12.8%
자동차세 5
12.8%
등록면허세 5
12.8%
지역자원시설세 5
12.8%
등록세 4
10.3%

Length

2023-12-13T06:36:27.217577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:36:27.486824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
교육세 5
12.8%
재산세 5
12.8%
주민세 5
12.8%
취득세 5
12.8%
자동차세 5
12.8%
등록면허세 5
12.8%
지역자원시설세 5
12.8%
등록세 4
10.3%

과세년도
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Memory size444.0 B
2017
8 
2018
8 
2020
8 
2021
8 
2019
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 (%)
2017 8
20.5%
2018 8
20.5%
2020 8
20.5%
2021 8
20.5%
2019 7
17.9%

Length

2023-12-13T06:36:27.780586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:36:28.013036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 8
20.5%
2018 8
20.5%
2020 8
20.5%
2021 8
20.5%
2019 7
17.9%

비과세금액
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct31
Distinct (%)86.1%
Missing3
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean1.3160696 × 109
Minimum0
Maximum8.767106 × 109
Zeros6
Zeros (%)15.4%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T06:36:28.749875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14408000
median52731000
Q39.194665 × 108
95-th percentile8.097548 × 109
Maximum8.767106 × 109
Range8.767106 × 109
Interquartile range (IQR)9.150585 × 108

Descriptive statistics

Standard deviation2.7896143 × 109
Coefficient of variation (CV)2.1196557
Kurtosis2.7859318
Mean1.3160696 × 109
Median Absolute Deviation (MAD)52731000
Skewness2.1101006
Sum4.7378504 × 1010
Variance7.7819479 × 1018
MonotonicityNot monotonic
2023-12-13T06:36:28.957010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 6
 
15.4%
8670000 1
 
2.6%
902916000 1
 
2.6%
195142000 1
 
2.6%
4321000 1
 
2.6%
53424000 1
 
2.6%
1276856000 1
 
2.6%
48242000 1
 
2.6%
7679033000 1
 
2.6%
154522000 1
 
2.6%
Other values (21) 21
53.8%
(Missing) 3
 
7.7%
ValueCountFrequency (%)
0 6
15.4%
250000 1
 
2.6%
4321000 1
 
2.6%
4363000 1
 
2.6%
4423000 1
 
2.6%
4506000 1
 
2.6%
6824000 1
 
2.6%
8170000 1
 
2.6%
8670000 1
 
2.6%
9400000 1
 
2.6%
ValueCountFrequency (%)
8767106000 1
2.6%
8410871000 1
2.6%
7993107000 1
2.6%
7679033000 1
2.6%
7549557000 1
2.6%
1508048000 1
2.6%
1276856000 1
2.6%
1213940000 1
2.6%
969118000 1
2.6%
902916000 1
2.6%

감면금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct39
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9929018 × 108
Minimum10000
Maximum6.971458 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T06:36:29.118637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile43300
Q17962000
median1.10441 × 108
Q37.001645 × 108
95-th percentile3.0228284 × 109
Maximum6.971458 × 109
Range6.971448 × 109
Interquartile range (IQR)6.922025 × 108

Descriptive statistics

Standard deviation1.3881566 × 109
Coefficient of variation (CV)1.9850939
Kurtosis10.715304
Mean6.9929018 × 108
Median Absolute Deviation (MAD)1.02678 × 108
Skewness3.0026693
Sum2.7272317 × 1010
Variance1.9269789 × 1018
MonotonicityNot monotonic
2023-12-13T06:36:29.282313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
10000 1
 
2.6%
2087000 1
 
2.6%
65636000 1
 
2.6%
49000 1
 
2.6%
1576000 1
 
2.6%
1219288000 1
 
2.6%
8161000 1
 
2.6%
2180129000 1
 
2.6%
170006000 1
 
2.6%
112374000 1
 
2.6%
Other values (29) 29
74.4%
ValueCountFrequency (%)
10000 1
2.6%
28000 1
2.6%
45000 1
2.6%
49000 1
2.6%
71000 1
2.6%
1576000 1
2.6%
2087000 1
2.6%
2986000 1
2.6%
6668000 1
2.6%
7763000 1
2.6%
ValueCountFrequency (%)
6971458000 1
2.6%
3798038000 1
2.6%
2936694000 1
2.6%
2285679000 1
2.6%
2180129000 1
2.6%
1652474000 1
2.6%
1507207000 1
2.6%
1416829000 1
2.6%
1383184000 1
2.6%
1219288000 1
2.6%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)94.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.7066143 × 109
Minimum0
Maximum3.8152991 × 1010
Zeros3
Zeros (%)7.7%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T06:36:29.436379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.6725715 × 109
median5.464861 × 109
Q31.1058694 × 1010
95-th percentile3.5709022 × 1010
Maximum3.8152991 × 1010
Range3.8152991 × 1010
Interquartile range (IQR)9.3861225 × 109

Descriptive statistics

Standard deviation1.095544 × 1010
Coefficient of variation (CV)1.2582894
Kurtosis2.011702
Mean8.7066143 × 109
Median Absolute Deviation (MAD)3.813923 × 109
Skewness1.7183347
Sum3.3955796 × 1011
Variance1.2002167 × 1020
MonotonicityNot monotonic
2023-12-13T06:36:29.604885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 3
 
7.7%
8482739000 1
 
2.6%
1861028000 1
 
2.6%
2048805000 1
 
2.6%
8332126000 1
 
2.6%
30741000 1
 
2.6%
14726387000 1
 
2.6%
1283733000 1
 
2.6%
31784507000 1
 
2.6%
5966489000 1
 
2.6%
Other values (27) 27
69.2%
ValueCountFrequency (%)
0 3
7.7%
30741000 1
 
2.6%
1233901000 1
 
2.6%
1265412000 1
 
2.6%
1283733000 1
 
2.6%
1304697000 1
 
2.6%
1353769000 1
 
2.6%
1650938000 1
 
2.6%
1694205000 1
 
2.6%
1753547000 1
 
2.6%
ValueCountFrequency (%)
38152991000 1
2.6%
37850329000 1
2.6%
35471099000 1
2.6%
31784507000 1
2.6%
27518011000 1
2.6%
15435079000 1
2.6%
14726387000 1
2.6%
14524002000 1
2.6%
13860476000 1
2.6%
13233479000 1
2.6%

비과세감면율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)82.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.502308
Minimum0
Maximum68.29
Zeros8
Zeros (%)20.5%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T06:36:29.725478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.34
median5.9
Q310.925
95-th percentile67.818
Maximum68.29
Range68.29
Interquartile range (IQR)9.585

Descriptive statistics

Standard deviation21.172633
Coefficient of variation (CV)1.5680751
Kurtosis2.9205022
Mean13.502308
Median Absolute Deviation (MAD)4.82
Skewness2.0833863
Sum526.59
Variance448.28039
MonotonicityNot monotonic
2023-12-13T06:36:29.865294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0.0 8
 
20.5%
9.7 1
 
2.6%
13.01 1
 
2.6%
5.9 1
 
2.6%
3.91 1
 
2.6%
11.13 1
 
2.6%
5.59 1
 
2.6%
60.46 1
 
2.6%
10.72 1
 
2.6%
6.28 1
 
2.6%
Other values (22) 22
56.4%
ValueCountFrequency (%)
0.0 8
20.5%
0.66 1
 
2.6%
1.32 1
 
2.6%
1.36 1
 
2.6%
1.39 1
 
2.6%
3.67 1
 
2.6%
3.72 1
 
2.6%
3.91 1
 
2.6%
4.02 1
 
2.6%
4.19 1
 
2.6%
ValueCountFrequency (%)
68.29 1
2.6%
67.89 1
2.6%
67.81 1
2.6%
67.5 1
2.6%
60.46 1
2.6%
22.2 1
2.6%
13.79 1
2.6%
13.14 1
2.6%
13.01 1
2.6%
11.13 1
2.6%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size444.0 B
Minimum2022-09-21 00:00:00
Maximum2022-09-21 00:00:00
2023-12-13T06:36:29.987302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:36:30.077190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-13T06:36:24.355046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:36:22.459606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:36:22.898929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:36:23.326673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:36:23.852056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:36:24.451171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:36:22.559472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:36:22.984354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:36:23.423230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:36:23.993257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:36:24.543894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:36:22.640532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:36:23.050303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:36:23.499547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:36:24.074339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:36:24.631776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:36:22.722384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:36:23.153991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:36:23.628384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:36:24.163522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:36:24.720353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:36:22.818963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:36:23.233377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:36:23.747916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:36:24.256373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:36:30.160864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번세목명과세년도비과세금액감면금액부과금액비과세감면율
순번1.0000.0001.0000.0000.0000.0000.230
세목명0.0001.0000.0000.9690.6570.9500.753
과세년도1.0000.0001.0000.0000.0000.0000.000
비과세금액0.0000.9690.0001.0000.8540.9900.874
감면금액0.0000.6570.0000.8541.0000.8380.840
부과금액0.0000.9500.0000.9900.8381.0000.921
비과세감면율0.2300.7530.0000.8740.8400.9211.000
2023-12-13T06:36:30.274068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도
세목명1.0000.000
과세년도0.0001.000
2023-12-13T06:36:30.355626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번비과세금액감면금액부과금액비과세감면율세목명과세년도
순번1.0000.0230.070-0.0150.1120.0000.924
비과세금액0.0231.0000.8010.5510.8660.7140.000
감면금액0.0700.8011.0000.6100.8060.4210.000
부과금액-0.0150.5510.6101.0000.5060.6450.000
비과세감면율0.1120.8660.8060.5061.0000.5260.000
세목명0.0000.7140.4210.6450.5261.0000.000
과세년도0.9240.0000.0000.0000.0000.0001.000

Missing values

2023-12-13T06:36:24.852296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:36:24.987747image/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

순번시도명시군구명자치단체코드세목명과세년도비과세금액감면금액부과금액비과세감면율데이터기준일자
01강원도평창군42760교육세201701000084827390000.02022-09-21
12강원도평창군42760등록세2017<NA>208700000.02022-09-21
23강원도평창군42760재산세2017754955700013831840001323347900067.52022-09-21
34강원도평창군42760주민세20178670000861400013046970001.322022-09-21
45강원도평창군42760취득세201790291600069714580003547109900022.22022-09-21
56강원도평창군42760자동차세20175603100017717800057948870004.022022-09-21
67강원도평창군42760등록면허세2017436300014032800016509380008.762022-09-21
78강원도평창군42760지역자원시설세20171142770007089500022054940008.42022-09-21
89강원도평창군42760교육세201802800087370640000.02022-09-21
910강원도평창군42760등록세2018<NA>298600000.02022-09-21
순번시도명시군구명자치단체코드세목명과세년도비과세금액감면금액부과금액비과세감면율데이터기준일자
2930강원도평창군42760등록면허세2020442300011237400018610280006.282022-09-21
3031강원도평창군42760지역자원시설세202015452200067221000206781200010.722022-09-21
3132강원도평창군42760교육세202107100088839090000.02022-09-21
3233강원도평창군42760등록세2021<NA>666800000.02022-09-21
3334강원도평창군42760재산세2021767903300016524740001543507900060.462022-09-21
3435강원도평창군42760주민세2021482420002076200012339010005.592022-09-21
3536강원도평창군42760취득세2021127685600029366940003785032900011.132022-09-21
3637강원도평창군42760자동차세20215342400016051100054648610003.912022-09-21
3738강원도평창군42760등록면허세2021432100011044100019437450005.92022-09-21
3839강원도평창군42760지역자원시설세202119514200068639000202789000013.012022-09-21