Overview

Dataset statistics

Number of variables10
Number of observations24
Missing cells3
Missing cells (%)1.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory92.5 B

Variable types

Numeric5
Categorical5

Dataset

Description전라남도 신안군 2017년 ~2019년 과세액 중 비과세액과 감면액이 차지하는 비율 현황을 제공하는 자료로 세목명, 비과세금액, 감면금액, 부과금액, 비과세감면율등을 포함하고 있습니다.
Author전라남도 신안군
URLhttps://www.data.go.kr/data/15079950/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
순번 is highly overall correlated with 과세년도High correlation
비과세금액 is highly overall correlated with 감면금액 and 1 other fieldsHigh correlation
감면금액 is highly overall correlated with 비과세금액 and 3 other fieldsHigh correlation
부과금액 is highly overall correlated with 감면금액 and 1 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
과세년도 is highly overall correlated with 순번High correlation
비과세금액 has 3 (12.5%) missing valuesMissing
순번 has unique valuesUnique
비과세금액 has 3 (12.5%) zerosZeros
부과금액 has 3 (12.5%) zerosZeros
비과세감면율 has 6 (25.0%) zerosZeros

Reproduction

Analysis started2023-12-12 09:26:17.332374
Analysis finished2023-12-12 09:26:21.906723
Duration4.57 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.5
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T18:26:21.997261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.15
Q16.75
median12.5
Q318.25
95-th percentile22.85
Maximum24
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation7.0710678
Coefficient of variation (CV)0.56568542
Kurtosis-1.2
Mean12.5
Median Absolute Deviation (MAD)6
Skewness0
Sum300
Variance50
MonotonicityStrictly increasing
2023-12-12T18:26:22.178674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 1
 
4.2%
14 1
 
4.2%
24 1
 
4.2%
23 1
 
4.2%
22 1
 
4.2%
21 1
 
4.2%
20 1
 
4.2%
19 1
 
4.2%
18 1
 
4.2%
17 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
1 1
4.2%
2 1
4.2%
3 1
4.2%
4 1
4.2%
5 1
4.2%
6 1
4.2%
7 1
4.2%
8 1
4.2%
9 1
4.2%
10 1
4.2%
ValueCountFrequency (%)
24 1
4.2%
23 1
4.2%
22 1
4.2%
21 1
4.2%
20 1
4.2%
19 1
4.2%
18 1
4.2%
17 1
4.2%
16 1
4.2%
15 1
4.2%

시도명
Categorical

CONSTANT 

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

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 24
100.0%

Length

2023-12-12T18:26:22.365699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:26:22.476747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라남도 24
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
신안군 24
100.0%

Length

2023-12-12T18:26:22.582869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:26:22.694539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신안군 24
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size324.0 B
46910
24 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row46910
2nd row46910
3rd row46910
4th row46910
5th row46910

Common Values

ValueCountFrequency (%)
46910 24
100.0%

Length

2023-12-12T18:26:22.819752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:26:22.961085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
46910 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

2023-12-12T18:26:23.122809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:26:23.324720image/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

HIGH CORRELATION 

Distinct3
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size324.0 B
2017
8 
2018
8 
2019
8 

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
33.3%
2018 8
33.3%
2019 8
33.3%

Length

2023-12-12T18:26:23.573029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:26:23.716778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 8
33.3%
2018 8
33.3%
2019 8
33.3%

비과세금액
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct19
Distinct (%)90.5%
Missing3
Missing (%)12.5%
Infinite0
Infinite (%)0.0%
Mean2.4068838 × 108
Minimum0
Maximum1.09411 × 109
Zeros3
Zeros (%)12.5%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T18:26:23.831268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17332000
median28826000
Q34.4915 × 108
95-th percentile1.038057 × 109
Maximum1.09411 × 109
Range1.09411 × 109
Interquartile range (IQR)4.41818 × 108

Descriptive statistics

Standard deviation3.7170022 × 108
Coefficient of variation (CV)1.5443214
Kurtosis0.50753796
Mean2.4068838 × 108
Median Absolute Deviation (MAD)28826000
Skewness1.4011396
Sum5.054456 × 109
Variance1.3816106 × 1017
MonotonicityNot monotonic
2023-12-12T18:26:24.016234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 3
 
12.5%
20270000 1
 
4.2%
449150000 1
 
4.2%
52250000 1
 
4.2%
7332000 1
 
4.2%
30725000 1
 
4.2%
814147000 1
 
4.2%
22800000 1
 
4.2%
1038057000 1
 
4.2%
90800000 1
 
4.2%
Other values (9) 9
37.5%
(Missing) 3
 
12.5%
ValueCountFrequency (%)
0 3
12.5%
2303000 1
 
4.2%
2306000 1
 
4.2%
7332000 1
 
4.2%
19040000 1
 
4.2%
20270000 1
 
4.2%
22800000 1
 
4.2%
26438000 1
 
4.2%
28826000 1
 
4.2%
30725000 1
 
4.2%
ValueCountFrequency (%)
1094110000 1
4.2%
1038057000 1
4.2%
814147000 1
4.2%
692061000 1
4.2%
583379000 1
4.2%
449150000 1
4.2%
90800000 1
4.2%
80462000 1
4.2%
52250000 1
4.2%
30725000 1
4.2%

감면금액
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1823125 × 108
Minimum4000
Maximum1.425173 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T18:26:24.168060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4000
5-th percentile5000
Q18634750
median44340000
Q31.730925 × 108
95-th percentile1.3026875 × 109
Maximum1.425173 × 109
Range1.425169 × 109
Interquartile range (IQR)1.6445775 × 108

Descriptive statistics

Standard deviation4.1301667 × 108
Coefficient of variation (CV)1.8925643
Kurtosis4.6586206
Mean2.1823125 × 108
Median Absolute Deviation (MAD)43222500
Skewness2.3918912
Sum5.23755 × 109
Variance1.7058277 × 1017
MonotonicityNot monotonic
2023-12-12T18:26:24.328789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
5000 2
 
8.3%
4000 1
 
4.2%
155923000 1
 
4.2%
20922000 1
 
4.2%
79312000 1
 
4.2%
156333000 1
 
4.2%
965711000 1
 
4.2%
11925000 1
 
4.2%
235000000 1
 
4.2%
7860000 1
 
4.2%
Other values (13) 13
54.2%
ValueCountFrequency (%)
4000 1
4.2%
5000 2
8.3%
2230000 1
4.2%
7777000 1
4.2%
7860000 1
4.2%
8893000 1
4.2%
10810000 1
4.2%
11925000 1
4.2%
18541000 1
4.2%
20315000 1
4.2%
ValueCountFrequency (%)
1425173000 1
4.2%
1362154000 1
4.2%
965711000 1
4.2%
235000000 1
4.2%
225719000 1
4.2%
223371000 1
4.2%
156333000 1
4.2%
155923000 1
4.2%
154582000 1
4.2%
79312000 1
4.2%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.906879 × 109
Minimum0
Maximum1.1256369 × 1010
Zeros3
Zeros (%)12.5%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T18:26:24.486531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.1719075 × 108
median1.6714475 × 109
Q33.7807465 × 109
95-th percentile9.9591036 × 109
Maximum1.1256369 × 1010
Range1.1256369 × 1010
Interquartile range (IQR)3.3635558 × 109

Descriptive statistics

Standard deviation3.3702469 × 109
Coefficient of variation (CV)1.1594039
Kurtosis0.79455023
Mean2.906879 × 109
Median Absolute Deviation (MAD)1.417125 × 109
Skewness1.3325976
Sum6.9765096 × 1010
Variance1.1358564 × 1019
MonotonicityNot monotonic
2023-12-12T18:26:24.639907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 3
 
12.5%
3038929000 1
 
4.2%
6386918000 1
 
4.2%
265588000 1
 
4.2%
1396851000 1
 
4.2%
5238223000 1
 
4.2%
11256369000 1
 
4.2%
484171000 1
 
4.2%
2236652000 1
 
4.2%
3294921000 1
 
4.2%
Other values (12) 12
50.0%
ValueCountFrequency (%)
0 3
12.5%
215040000 1
 
4.2%
251717000 1
 
4.2%
265588000 1
 
4.2%
467725000 1
 
4.2%
484142000 1
 
4.2%
484171000 1
 
4.2%
964914000 1
 
4.2%
1348425000 1
 
4.2%
1396851000 1
 
4.2%
ValueCountFrequency (%)
11256369000 1
4.2%
10155122000 1
4.2%
8848333000 1
4.2%
6386918000 1
4.2%
6254265000 1
4.2%
5238223000 1
4.2%
3294921000 1
4.2%
3085967000 1
4.2%
3038929000 1
4.2%
2236652000 1
4.2%

비과세감면율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.164583
Minimum0
Maximum61.54
Zeros6
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T18:26:24.827530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.1675
median6.22
Q322.7725
95-th percentile55.438
Maximum61.54
Range61.54
Interquartile range (IQR)20.605

Descriptive statistics

Standard deviation19.948509
Coefficient of variation (CV)1.2340874
Kurtosis0.089560336
Mean16.164583
Median Absolute Deviation (MAD)6.22
Skewness1.2141786
Sum387.95
Variance397.94302
MonotonicityNot monotonic
2023-12-12T18:26:24.996324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0.0 6
25.0%
2.89 2
 
8.3%
5.2 1
 
4.2%
27.55 1
 
4.2%
6.2 1
 
4.2%
3.57 1
 
4.2%
15.81 1
 
4.2%
7.17 1
 
4.2%
56.92 1
 
4.2%
44.14 1
 
4.2%
Other values (8) 8
33.3%
ValueCountFrequency (%)
0.0 6
25.0%
2.89 2
 
8.3%
3.57 1
 
4.2%
5.2 1
 
4.2%
6.17 1
 
4.2%
6.2 1
 
4.2%
6.24 1
 
4.2%
7.17 1
 
4.2%
8.24 1
 
4.2%
15.81 1
 
4.2%
ValueCountFrequency (%)
61.54 1
4.2%
56.92 1
4.2%
47.04 1
4.2%
46.04 1
4.2%
44.14 1
4.2%
27.55 1
4.2%
21.18 1
4.2%
19.16 1
4.2%
15.81 1
4.2%
8.24 1
4.2%

Interactions

2023-12-12T18:26:20.936743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:26:17.814103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:26:18.747041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:26:19.684217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:26:20.301311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:26:21.089678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:26:18.002700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:26:18.905175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:26:19.781093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:26:20.428584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:26:21.206161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:26:18.184858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:26:19.028459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:26:19.897753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:26:20.530019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:26:21.363867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:26:18.380899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:26:19.482535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:26:20.044131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:26:20.651775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:26:21.476212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:26:18.581204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:26:19.580460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:26:20.181944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:26:20.789425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:26:25.129117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번세목명과세년도비과세금액감면금액부과금액비과세감면율
순번1.0000.0000.9020.6270.2330.0000.000
세목명0.0001.0000.0000.5870.9550.8650.815
과세년도0.9020.0001.0000.0000.0000.0000.000
비과세금액0.6270.5870.0001.0000.9510.7360.858
감면금액0.2330.9550.0000.9511.0000.8220.892
부과금액0.0000.8650.0000.7360.8221.0000.739
비과세감면율0.0000.8150.0000.8580.8920.7391.000
2023-12-12T18:26:25.275556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명
과세년도1.0000.000
세목명0.0001.000
2023-12-12T18:26:25.405553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번비과세금액감면금액부과금액비과세감면율세목명과세년도
순번1.0000.1110.2220.0690.1320.0000.696
비과세금액0.1111.0000.7800.1590.8300.3650.000
감면금액0.2220.7801.0000.5690.6770.6450.000
부과금액0.0690.1590.5691.0000.0640.6670.000
비과세감면율0.1320.8300.6770.0641.0000.5870.000
세목명0.0000.3650.6450.6670.5871.0000.000
과세년도0.6960.0000.0000.0000.0000.0001.000

Missing values

2023-12-12T18:26:21.634639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:26:21.822650image/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

순번시도명시군구명자치단체코드세목명과세년도비과세금액감면금액부과금액비과세감면율
01전라남도신안군46910교육세20170400030389290000.0
12전라남도신안군46910등록세2017<NA>223000000.0
23전라남도신안군46910재산세2017692061000223371000194604400047.04
34전라남도신안군46910주민세20172027000088930004677250006.24
45전라남도신안군46910취득세20174491500001425173000884833300021.18
56전라남도신안군46910자동차세20172643800015458200062542650002.89
67전라남도신안군46910등록면허세20172306000772270009649140008.24
78전라남도신안군46910지역자원시설세2017804620001854100021504000046.04
89전라남도신안군46910교육세20180500030859670000.0
910전라남도신안군46910등록세2018<NA>777700000.0
순번시도명시군구명자치단체코드세목명과세년도비과세금액감면금액부과금액비과세감면율
1415전라남도신안군46910등록면허세201823030006775800013484250005.2
1516전라남도신안군46910지역자원시설세2018908000002031500025171700044.14
1617전라남도신안군46910교육세20190500032949210000.0
1718전라남도신안군46910등록세2019<NA>786000000.0
1819전라남도신안군46910재산세20191038057000235000000223665200056.92
1920전라남도신안군46910주민세201922800000119250004841710007.17
2021전라남도신안군46910취득세20198141470009657110001125636900015.81
2122전라남도신안군46910자동차세20193072500015633300052382230003.57
2223전라남도신안군46910등록면허세201973320007931200013968510006.2
2324전라남도신안군46910지역자원시설세2019522500002092200026558800027.55