Overview

Dataset statistics

Number of variables10
Number of observations24
Missing cells1
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 KiB
Average record size in memory91.5 B

Variable types

Categorical5
Numeric4
DateTime1

Dataset

Description상기 데이터는 연도별 과세액 중 비과세액과 감면액이 차지하는 비율 현황을 제공하여 국민 조세 혜택 규모를 파악하는데 사용
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=349&beforeMenuCd=DOM_000000201001001000&publicdatapk=15080020

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
데이터기준일자 has constant value ""Constant
비과세금액 is highly overall correlated with 감면금액 and 2 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 감면금액 and 2 other fieldsHigh correlation
비과세금액 has 1 (4.2%) missing valuesMissing
감면금액 has unique valuesUnique
비과세금액 has 8 (33.3%) zerosZeros
부과금액 has 2 (8.3%) zerosZeros
비과세감면율 has 5 (20.8%) zerosZeros

Reproduction

Analysis started2024-01-09 20:57:36.273198
Analysis finished2024-01-09 20:57:38.021242
Duration1.75 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size324.0 B
충청남도
24 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
충청남도 24
100.0%

Length

2024-01-10T05:57:38.078115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:57:38.154913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청남도 24
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size324.0 B
부여군
24 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부여군 24
100.0%

Length

2024-01-10T05:57:38.244909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:57:38.322313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부여군 24
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size324.0 B
44760
24 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row44760
2nd row44760
3rd row44760
4th row44760
5th row44760

Common Values

ValueCountFrequency (%)
44760 24
100.0%

Length

2024-01-10T05:57:38.401040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:57:38.476742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
44760 24
100.0%

세목명
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size324.0 B
교육세
3 
등록세
3 
재산세
3 
주민세
3 
취득세
3 
Other values (3)
9 

Length

Max length7
Median length3
Mean length3.875
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
교육세 3
12.5%
등록세 3
12.5%
재산세 3
12.5%
주민세 3
12.5%
취득세 3
12.5%
자동차세 3
12.5%
등록면허세 3
12.5%
지역자원시설세 3
12.5%

Length

2024-01-10T05:57:38.569467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:57:38.682005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
교육세 3
12.5%
등록세 3
12.5%
재산세 3
12.5%
주민세 3
12.5%
취득세 3
12.5%
자동차세 3
12.5%
등록면허세 3
12.5%
지역자원시설세 3
12.5%

과세년도
Categorical

Distinct3
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size324.0 B
2020
8 
2021
8 
2022
8 

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 8
33.3%
2021 8
33.3%
2022 8
33.3%

Length

2024-01-10T05:57:38.799652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:57:38.879578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 8
33.3%
2021 8
33.3%
2022 8
33.3%

비과세금액
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct16
Distinct (%)69.6%
Missing1
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean1.0668147 × 109
Minimum0
Maximum6.288728 × 109
Zeros8
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-01-10T05:57:38.960299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median45170000
Q39.8695 × 108
95-th percentile5.6511198 × 109
Maximum6.288728 × 109
Range6.288728 × 109
Interquartile range (IQR)9.8695 × 108

Descriptive statistics

Standard deviation2.0017915 × 109
Coefficient of variation (CV)1.8764191
Kurtosis2.1953835
Mean1.0668147 × 109
Median Absolute Deviation (MAD)45170000
Skewness1.8546176
Sum2.4536738 × 1010
Variance4.0071692 × 1018
MonotonicityNot monotonic
2024-01-10T05:57:39.060254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 8
33.3%
6792000 1
 
4.2%
164805000 1
 
4.2%
8740000 1
 
4.2%
49245000 1
 
4.2%
1809095000 1
 
4.2%
6288728000 1
 
4.2%
154953000 1
 
4.2%
50979000 1
 
4.2%
5179779000 1
 
4.2%
Other values (6) 6
25.0%
ValueCountFrequency (%)
0 8
33.3%
6792000 1
 
4.2%
8740000 1
 
4.2%
14165000 1
 
4.2%
45170000 1
 
4.2%
49245000 1
 
4.2%
50979000 1
 
4.2%
152625000 1
 
4.2%
154953000 1
 
4.2%
164805000 1
 
4.2%
ValueCountFrequency (%)
6288728000 1
4.2%
5703491000 1
4.2%
5179779000 1
4.2%
2568335000 1
4.2%
2339836000 1
4.2%
1809095000 1
4.2%
164805000 1
4.2%
154953000 1
4.2%
152625000 1
4.2%
50979000 1
4.2%

감면금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6001225 × 108
Minimum5000
Maximum3.52793 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-01-10T05:57:39.170137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5000
5-th percentile17250
Q110799500
median55167500
Q34.5878125 × 108
95-th percentile3.0780498 × 109
Maximum3.52793 × 109
Range3.527925 × 109
Interquartile range (IQR)4.4798175 × 108

Descriptive statistics

Standard deviation1.0559846 × 109
Coefficient of variation (CV)1.8856455
Kurtosis3.5962647
Mean5.6001225 × 108
Median Absolute Deviation (MAD)55124000
Skewness2.1949862
Sum1.3440294 × 1010
Variance1.1151035 × 1018
MonotonicityNot monotonic
2024-01-10T05:57:39.278234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
5000 1
 
4.2%
333923000 1
 
4.2%
36686000 1
 
4.2%
73649000 1
 
4.2%
303165000 1
 
4.2%
3093748000 1
 
4.2%
1921000 1
 
4.2%
781574000 1
 
4.2%
23348000 1
 
4.2%
81000 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
5000 1
4.2%
6000 1
4.2%
81000 1
4.2%
630000 1
4.2%
665000 1
4.2%
1921000 1
4.2%
13759000 1
4.2%
14288000 1
4.2%
23348000 1
4.2%
35267000 1
4.2%
ValueCountFrequency (%)
3527930000 1
4.2%
3093748000 1
4.2%
2989093000 1
4.2%
809093000 1
4.2%
781574000 1
4.2%
765010000 1
4.2%
356705000 1
4.2%
333923000 1
4.2%
303165000 1
4.2%
137677000 1
4.2%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4920376 × 109
Minimum0
Maximum1.7886522 × 1010
Zeros2
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-01-10T05:57:39.652513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9937800
Q11.0963378 × 109
median3.564776 × 109
Q37.7935668 × 109
95-th percentile1.7254532 × 1010
Maximum1.7886522 × 1010
Range1.7886522 × 1010
Interquartile range (IQR)6.697229 × 109

Descriptive statistics

Standard deviation5.7284442 × 109
Coefficient of variation (CV)1.0430453
Kurtosis0.046608638
Mean5.4920376 × 109
Median Absolute Deviation (MAD)2.928481 × 109
Skewness1.0447561
Sum1.318089 × 1011
Variance3.2815073 × 1019
MonotonicityNot monotonic
2024-01-10T05:57:39.753014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 2
 
8.3%
5772631000 1
 
4.2%
12600588000 1
 
4.2%
974075000 1
 
4.2%
1137092000 1
 
4.2%
9389407000 1
 
4.2%
16342145000 1
 
4.2%
1407295000 1
 
4.2%
7414492000 1
 
4.2%
5716731000 1
 
4.2%
Other values (13) 13
54.2%
ValueCountFrequency (%)
0 2
8.3%
66252000 1
4.2%
927573000 1
4.2%
940499000 1
4.2%
974075000 1
4.2%
1137092000 1
4.2%
1300402000 1
4.2%
1306413000 1
4.2%
1385382000 1
4.2%
1407295000 1
4.2%
ValueCountFrequency (%)
17886522000 1
4.2%
17415542000 1
4.2%
16342145000 1
4.2%
12600588000 1
4.2%
9389407000 1
4.2%
8930791000 1
4.2%
7414492000 1
4.2%
6960114000 1
4.2%
6784535000 1
4.2%
5772631000 1
4.2%

비과세감면율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.489167
Minimum0
Maximum95.36
Zeros5
Zeros (%)20.8%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-01-10T05:57:39.870490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.05
median7.705
Q323.6775
95-th percentile92.6775
Maximum95.36
Range95.36
Interquartile range (IQR)23.6275

Descriptive statistics

Standard deviation29.812785
Coefficient of variation (CV)1.4550511
Kurtosis2.4268359
Mean20.489167
Median Absolute Deviation (MAD)7.705
Skewness1.8423266
Sum491.74
Variance888.80213
MonotonicityNot monotonic
2024-01-10T05:57:39.973070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0.0 5
20.8%
0.05 2
 
8.3%
8.16 1
 
4.2%
20.69 1
 
4.2%
7.25 1
 
4.2%
3.75 1
 
4.2%
30.0 1
 
4.2%
0.14 1
 
4.2%
95.36 1
 
4.2%
20.23 1
 
4.2%
Other values (9) 9
37.5%
ValueCountFrequency (%)
0.0 5
20.8%
0.05 2
 
8.3%
0.14 1
 
4.2%
3.05 1
 
4.2%
3.75 1
 
4.2%
4.5 1
 
4.2%
7.25 1
 
4.2%
8.16 1
 
4.2%
10.75 1
 
4.2%
20.23 1
 
4.2%
ValueCountFrequency (%)
95.36 1
4.2%
93.57 1
4.2%
87.62 1
4.2%
34.08 1
4.2%
30.6 1
4.2%
30.0 1
4.2%
21.57 1
4.2%
20.69 1
4.2%
20.32 1
4.2%
20.23 1
4.2%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size324.0 B
Minimum2023-09-30 00:00:00
Maximum2023-09-30 00:00:00
2024-01-10T05:57:40.057951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:57:40.134191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-01-10T05:57:37.506944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:57:36.528752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:57:36.860906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:57:37.190394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:57:37.592309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:57:36.614299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:57:36.941775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:57:37.270405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:57:37.672817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:57:36.714698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:57:37.025530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:57:37.353778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:57:37.751424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:57:36.786429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:57:37.113275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:57:37.430688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T05:57:40.198488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도비과세금액감면금액부과금액비과세감면율
세목명1.0000.0000.5810.7740.8140.895
과세년도0.0001.0000.0000.0000.0000.000
비과세금액0.5810.0001.0000.8880.6800.731
감면금액0.7740.0000.8881.0000.7170.931
부과금액0.8140.0000.6800.7171.0000.597
비과세감면율0.8950.0000.7310.9310.5971.000
2024-01-10T05:57:40.293643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명
과세년도1.0000.000
세목명0.0001.000
2024-01-10T05:57:40.377903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비과세금액감면금액부과금액비과세감면율세목명과세년도
비과세금액1.0000.8630.5070.8710.3250.000
감면금액0.8631.0000.6320.8010.5710.000
부과금액0.5070.6321.0000.3630.5860.000
비과세감면율0.8710.8010.3631.0000.7480.000
세목명0.3250.5710.5860.7481.0000.000
과세년도0.0000.0000.0000.0000.0001.000

Missing values

2024-01-10T05:57:37.852249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T05:57:37.972588image/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

시도명시군구명자치단체코드세목명과세년도비과세금액감면금액부과금액비과세감면율데이터기준일자
0충청남도부여군44760교육세20200500057726310000.02023-09-30
1충청남도부여군44760등록세20200142880006625200021.572023-09-30
2충청남도부여군44760재산세20205179779000765010000678453500087.622023-09-30
3충청남도부여군44760주민세2020063000013064130000.052023-09-30
4충청남도부여군44760취득세2020256833500035279300001788652200034.082023-09-30
5충청남도부여군44760자동차세20204517000035670500089307910004.52023-09-30
6충청남도부여군44760등록면허세202014165000137677000141282100010.752023-09-30
7충청남도부여군44760지역자원시설세20201526250003582200092757300020.322023-09-30
8충청남도부여군44760교육세20210600057376010000.02023-09-30
9충청남도부여군44760등록세202101375900000.02023-09-30
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