Overview

Dataset statistics

Number of variables9
Number of observations47
Missing cells16
Missing cells (%)3.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 KiB
Average record size in memory80.8 B

Variable types

Categorical4
Numeric5

Dataset

Description경기도 이천시 지방세 비과감면율 현황에 대한 과세연도, 세목명, 비과세금액, 감면금액, 부과금액, 비과세감면율 정보를 제공
Author경기도 이천시
URLhttps://www.data.go.kr/data/15079213/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
지방자치단체코드 has constant value ""Constant
비과세금액 is highly overall correlated with 감면금액 and 3 other fieldsHigh correlation
감면금액 is highly overall correlated with 비과세금액 and 3 other fieldsHigh correlation
부과금액 is highly overall correlated with 비과세금액 and 2 other fieldsHigh correlation
비과세감면율 is highly overall correlated with 비과세금액 and 2 other fieldsHigh correlation
세목명 is highly overall correlated with 비과세금액 and 3 other fieldsHigh correlation
비과세금액 has 11 (23.4%) missing valuesMissing
부과금액 has 5 (10.6%) missing valuesMissing
비과세감면율 has 10 (21.3%) zerosZeros

Reproduction

Analysis started2023-12-12 06:55:31.647143
Analysis finished2023-12-12 06:55:34.672778
Duration3.03 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size508.0 B
경기도
47 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 47
100.0%

Length

2023-12-12T15:55:34.741214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:55:34.840044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 47
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size508.0 B
이천시
47 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
이천시 47
100.0%

Length

2023-12-12T15:55:34.946176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:55:35.054939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
이천시 47
100.0%

지방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size508.0 B
41500
47 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row41500
2nd row41500
3rd row41500
4th row41500
5th row41500

Common Values

ValueCountFrequency (%)
41500 47
100.0%

Length

2023-12-12T15:55:35.172089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:55:35.299860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
41500 47
100.0%

과세연도
Real number (ℝ)

Distinct6
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.5532
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-12T15:55:35.412047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018
median2020
Q32021
95-th percentile2022
Maximum2022
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7043233
Coefficient of variation (CV)0.00084391106
Kurtosis-1.2483096
Mean2019.5532
Median Absolute Deviation (MAD)1
Skewness-0.026160522
Sum94919
Variance2.9047179
MonotonicityIncreasing
2023-12-12T15:55:35.556829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2018 8
17.0%
2019 8
17.0%
2020 8
17.0%
2021 8
17.0%
2022 8
17.0%
2017 7
14.9%
ValueCountFrequency (%)
2017 7
14.9%
2018 8
17.0%
2019 8
17.0%
2020 8
17.0%
2021 8
17.0%
2022 8
17.0%
ValueCountFrequency (%)
2022 8
17.0%
2021 8
17.0%
2020 8
17.0%
2019 8
17.0%
2018 8
17.0%
2017 7
14.9%

세목명
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Memory size508.0 B
등록세
6 
재산세
6 
주민세
6 
취득세
6 
자동차세
6 
Other values (3)
17 

Length

Max length7
Median length3
Mean length3.893617
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
등록세 6
12.8%
재산세 6
12.8%
주민세 6
12.8%
취득세 6
12.8%
자동차세 6
12.8%
등록면허세 6
12.8%
지역자원시설세 6
12.8%
교육세 5
10.6%

Length

2023-12-12T15:55:35.735684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:55:35.893902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
등록세 6
12.8%
재산세 6
12.8%
주민세 6
12.8%
취득세 6
12.8%
자동차세 6
12.8%
등록면허세 6
12.8%
지역자원시설세 6
12.8%
교육세 5
10.6%

비과세금액
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct36
Distinct (%)100.0%
Missing11
Missing (%)23.4%
Infinite0
Infinite (%)0.0%
Mean3.6580608 × 109
Minimum4698000
Maximum1.9190986 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-12T15:55:36.055263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4698000
5-th percentile7731000
Q134500500
median4.657265 × 108
Q35.4505482 × 109
95-th percentile1.6550564 × 1010
Maximum1.9190986 × 1010
Range1.9186288 × 1010
Interquartile range (IQR)5.4160478 × 109

Descriptive statistics

Standard deviation5.8389213 × 109
Coefficient of variation (CV)1.5961794
Kurtosis1.1646702
Mean3.6580608 × 109
Median Absolute Deviation (MAD)4.490735 × 108
Skewness1.5727141
Sum1.3169019 × 1011
Variance3.4093002 × 1019
MonotonicityNot monotonic
2023-12-12T15:55:36.216035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
10150000 1
 
2.1%
177812000 1
 
2.1%
23167000 1
 
2.1%
608668000 1
 
2.1%
17514777000 1
 
2.1%
8450000 1
 
2.1%
6212851000 1
 
2.1%
177116000 1
 
2.1%
4698000 1
 
2.1%
600012000 1
 
2.1%
Other values (26) 26
55.3%
(Missing) 11
23.4%
ValueCountFrequency (%)
4698000 1
2.1%
6474000 1
2.1%
8150000 1
2.1%
8450000 1
2.1%
10150000 1
2.1%
16151000 1
2.1%
17155000 1
2.1%
23167000 1
2.1%
30602000 1
2.1%
35800000 1
2.1%
ValueCountFrequency (%)
19190986000 1
2.1%
17514777000 1
2.1%
16229160000 1
2.1%
14106195000 1
2.1%
13474410000 1
2.1%
12881751000 1
2.1%
7129769000 1
2.1%
6212851000 1
2.1%
5524454000 1
2.1%
5425913000 1
2.1%

감면금액
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.845349 × 109
Minimum4000
Maximum2.1684777 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-12T15:55:36.376372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4000
5-th percentile10000
Q179850000
median2.73601 × 108
Q32.364406 × 109
95-th percentile1.5664379 × 1010
Maximum2.1684777 × 1010
Range2.1684773 × 1010
Interquartile range (IQR)2.284556 × 109

Descriptive statistics

Standard deviation5.6604472 × 109
Coefficient of variation (CV)1.9893683
Kurtosis4.2259698
Mean2.845349 × 109
Median Absolute Deviation (MAD)2.72471 × 108
Skewness2.3020618
Sum1.337314 × 1011
Variance3.2040662 × 1019
MonotonicityNot monotonic
2023-12-12T15:55:36.820190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
10000 2
 
4.3%
1103907000 1
 
2.1%
84135000 1
 
2.1%
20560788000 1
 
2.1%
1103698000 1
 
2.1%
300121000 1
 
2.1%
154292000 1
 
2.1%
1130000 1
 
2.1%
4205242000 1
 
2.1%
79109000 1
 
2.1%
Other values (36) 36
76.6%
ValueCountFrequency (%)
4000 1
2.1%
7000 1
2.1%
10000 2
4.3%
11000 1
2.1%
1130000 1
2.1%
1401000 1
2.1%
4671000 1
2.1%
10707000 1
2.1%
20837000 1
2.1%
38150000 1
2.1%
ValueCountFrequency (%)
21684777000 1
2.1%
20560788000 1
2.1%
15901433000 1
2.1%
15111254000 1
2.1%
14805585000 1
2.1%
12774695000 1
2.1%
4205242000 1
2.1%
4095971000 1
2.1%
3911711000 1
2.1%
3775512000 1
2.1%

부과금액
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct42
Distinct (%)100.0%
Missing5
Missing (%)10.6%
Infinite0
Infinite (%)0.0%
Mean5.7876043 × 1010
Minimum81453000
Maximum3.220891 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-12T15:55:36.949514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum81453000
5-th percentile8.3430458 × 109
Q11.3088212 × 1010
median3.981865 × 1010
Q36.0304008 × 1010
95-th percentile1.859401 × 1011
Maximum3.220891 × 1011
Range3.2200765 × 1011
Interquartile range (IQR)4.7215796 × 1010

Descriptive statistics

Standard deviation7.0553883 × 1010
Coefficient of variation (CV)1.2190516
Kurtosis5.5212131
Mean5.7876043 × 1010
Median Absolute Deviation (MAD)2.572362 × 1010
Skewness2.3304688
Sum2.4307938 × 1012
Variance4.9778504 × 1021
MonotonicityNot monotonic
2023-12-12T15:55:37.098513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
59604113000 1
 
2.1%
186515321000 1
 
2.1%
60537306000 1
 
2.1%
13716003000 1
 
2.1%
10817313000 1
 
2.1%
52998653000 1
 
2.1%
69914358000 1
 
2.1%
24040841000 1
 
2.1%
322089101000 1
 
2.1%
12878948000 1
 
2.1%
Other values (32) 32
68.1%
(Missing) 5
 
10.6%
ValueCountFrequency (%)
81453000 1
2.1%
7480170000 1
2.1%
8317954000 1
2.1%
8819791000 1
2.1%
9078017000 1
2.1%
9767737000 1
2.1%
10817313000 1
2.1%
10973921000 1
2.1%
12065690000 1
2.1%
12442632000 1
2.1%
ValueCountFrequency (%)
322089101000 1
2.1%
272012878000 1
2.1%
186515321000 1
2.1%
175011000000 1
2.1%
155415000000 1
2.1%
149998000000 1
2.1%
77630032000 1
2.1%
69914358000 1
2.1%
65500682000 1
2.1%
61691430000 1
2.1%

비과세감면율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)66.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2885106
Minimum0
Maximum31.07
Zeros10
Zeros (%)21.3%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-12T15:55:37.260677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.395
median3
Q38.5
95-th percentile30
Maximum31.07
Range31.07
Interquartile range (IQR)8.105

Descriptive statistics

Standard deviation9.6875426
Coefficient of variation (CV)1.3291526
Kurtosis1.451565
Mean7.2885106
Median Absolute Deviation (MAD)3
Skewness1.6249181
Sum342.56
Variance93.848483
MonotonicityNot monotonic
2023-12-12T15:55:37.401289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0.0 10
21.3%
3.0 4
 
8.5%
1.0 3
 
6.4%
30.0 2
 
4.3%
29.0 2
 
4.3%
2.51 1
 
2.1%
31.07 1
 
2.1%
0.36 1
 
2.1%
6.87 1
 
2.1%
2.15 1
 
2.1%
Other values (21) 21
44.7%
ValueCountFrequency (%)
0.0 10
21.3%
0.3 1
 
2.1%
0.36 1
 
2.1%
0.43 1
 
2.1%
1.0 3
 
6.4%
2.12 1
 
2.1%
2.15 1
 
2.1%
2.36 1
 
2.1%
2.45 1
 
2.1%
2.51 1
 
2.1%
ValueCountFrequency (%)
31.07 1
2.1%
30.75 1
2.1%
30.0 2
4.3%
29.0 2
4.3%
17.0 1
2.1%
13.54 1
2.1%
13.0 1
2.1%
12.0 1
2.1%
10.0 1
2.1%
9.0 1
2.1%

Interactions

2023-12-12T15:55:33.817045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:31.868902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:32.335853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:32.798553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:33.238553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:33.921115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:31.967647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:32.417202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:32.888655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:33.347631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:34.026558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:32.072293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:32.521568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:32.981821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:33.446098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:34.109920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:32.159098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:32.614796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:33.062710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:33.561287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:34.205050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:32.245473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:32.707134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:33.148169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:55:33.721206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:55:37.503001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세연도세목명비과세금액감면금액부과금액비과세감면율
과세연도1.0000.0000.0000.0000.0000.000
세목명0.0001.0000.7120.7480.9030.761
비과세금액0.0000.7121.0000.8560.9320.796
감면금액0.0000.7480.8561.0000.8870.835
부과금액0.0000.9030.9320.8871.0000.884
비과세감면율0.0000.7610.7960.8350.8841.000
2023-12-12T15:55:37.627161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세연도비과세금액감면금액부과금액비과세감면율세목명
과세연도1.000-0.027-0.0390.175-0.1360.000
비과세금액-0.0271.0000.6710.6170.8740.516
감면금액-0.0390.6711.0000.6420.8180.527
부과금액0.1750.6170.6421.0000.2940.517
비과세감면율-0.1360.8740.8180.2941.0000.532
세목명0.0000.5160.5270.5170.5321.000

Missing values

2023-12-12T15:55:34.328633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:55:34.496135image/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.
2023-12-12T15:55:34.613762image/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

시도명시군구명지방자치단체코드과세연도세목명비과세금액감면금액부과금액비과세감면율
0경기도이천시415002017등록세<NA>10707000<NA>0.0
1경기도이천시415002017재산세1288175100037755120005559138600030.0
2경기도이천시415002017주민세3580000080743000158007270001.0
3경기도이천시415002017취득세71297690001277469500014999800000013.0
4경기도이천시415002017자동차세341964000811070000442852880003.0
5경기도이천시415002017등록면허세647400046524900083179540006.0
6경기도이천시415002017지역자원시설세527656000210395000748017000010.0
7경기도이천시415002018교육세<NA>4000365296100000.0
8경기도이천시415002018등록세<NA>38150000<NA>0.0
9경기도이천시415002018재산세1347441000037014220005865997900029.0
시도명시군구명지방자치단체코드과세연도세목명비과세금액감면금액부과금액비과세감면율
37경기도이천시415002021등록면허세4698000318376000128789480002.51
38경기도이천시415002021지역자원시설세600012000167248000124426320006.17
39경기도이천시415002022교육세<NA>11000501753460000.0
40경기도이천시415002022등록세<NA>20837000<NA>0.0
41경기도이천시415002022재산세1919098600040959710007763003200030.0
42경기도이천시415002022주민세815000080591000297967310000.3
43경기도이천시415002022취득세5524454000148055850002720128780007.47
44경기도이천시415002022자동차세1768510001098427000441878720002.89
45경기도이천시415002022등록면허세16151000279705000120656900002.45
46경기도이천시415002022지역자원시설세709971000181596000140534430006.34