Overview

Dataset statistics

Number of variables9
Number of observations113
Missing cells11
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.7 KiB
Average record size in memory79.2 B

Variable types

Categorical5
Numeric4

Dataset

Description성남시 지방세 금액 중 비과세·감면액이 차지하는 비율에 대한 데이터로, 세목명, 과세년도, 비과세금액, 감면금액, 부과금액, 비과세감면율 목록으로 구성되어 있습니다
URLhttps://www.data.go.kr/data/15080589/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 is highly overall correlated with 자치단체코드High correlation
자치단체코드 is highly overall correlated with 시군구명High correlation
비과세금액 is highly overall correlated with 감면금액 and 1 other fieldsHigh correlation
감면금액 is highly overall correlated with 비과세금액 and 2 other fieldsHigh correlation
부과금액 is highly overall correlated with 감면금액High correlation
비과세감면율 is highly overall correlated with 비과세금액 and 1 other fieldsHigh correlation
비과세금액 has 11 (9.7%) missing valuesMissing
비과세금액 has 12 (10.6%) zerosZeros
부과금액 has 11 (9.7%) zerosZeros
비과세감면율 has 27 (23.9%) zerosZeros

Reproduction

Analysis started2023-12-12 11:12:52.016046
Analysis finished2023-12-12 11:12:55.599854
Duration3.58 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
경기도
113 

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 113
100.0%

Length

2023-12-12T20:12:55.730089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:12:55.860142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 113
100.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
성남시중원구
38 
성남시분당구
38 
성남시수정구
37 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
성남시중원구 38
33.6%
성남시분당구 38
33.6%
성남시수정구 37
32.7%

Length

2023-12-12T20:12:56.032241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:12:56.203906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
성남시중원구 38
33.6%
성남시분당구 38
33.6%
성남시수정구 37
32.7%

자치단체코드
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
41133
38 
41135
38 
41131
37 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row41131
2nd row41131
3rd row41131
4th row41131
5th row41131

Common Values

ValueCountFrequency (%)
41133 38
33.6%
41135 38
33.6%
41131 37
32.7%

Length

2023-12-12T20:12:56.378792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:12:56.546801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
41133 38
33.6%
41135 38
33.6%
41131 37
32.7%

세목명
Categorical

Distinct8
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
재산세
15 
주민세
15 
취득세
15 
자동차세
15 
등록면허세
15 
Other values (3)
38 

Length

Max length7
Median length3
Mean length3.9292035
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
재산세 15
13.3%
주민세 15
13.3%
취득세 15
13.3%
자동차세 15
13.3%
등록면허세 15
13.3%
지역자원시설세 15
13.3%
교육세 12
10.6%
등록세 11
9.7%

Length

2023-12-12T20:12:56.757374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:12:56.982671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
재산세 15
13.3%
주민세 15
13.3%
취득세 15
13.3%
자동차세 15
13.3%
등록면허세 15
13.3%
지역자원시설세 15
13.3%
교육세 12
10.6%
등록세 11
9.7%

과세년도
Categorical

Distinct5
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2018
23 
2019
23 
2021
23 
2017
22 
2022
22 

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 (%)
2018 23
20.4%
2019 23
20.4%
2021 23
20.4%
2017 22
19.5%
2022 22
19.5%

Length

2023-12-12T20:12:57.271757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:12:57.518015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 23
20.4%
2019 23
20.4%
2021 23
20.4%
2017 22
19.5%
2022 22
19.5%

비과세금액
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct91
Distinct (%)89.2%
Missing11
Missing (%)9.7%
Infinite0
Infinite (%)0.0%
Mean8.7601606 × 109
Minimum0
Maximum1.27692 × 1011
Zeros12
Zeros (%)10.6%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T20:12:57.776979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113421750
median1.68278 × 108
Q34.237808 × 109
95-th percentile4.2794632 × 1010
Maximum1.27692 × 1011
Range1.27692 × 1011
Interquartile range (IQR)4.2243862 × 109

Descriptive statistics

Standard deviation2.3706269 × 1010
Coefficient of variation (CV)2.7061455
Kurtosis13.514614
Mean8.7601606 × 109
Median Absolute Deviation (MAD)1.68278 × 108
Skewness3.6823593
Sum8.9353638 × 1011
Variance5.619872 × 1020
MonotonicityNot monotonic
2023-12-12T20:12:58.144311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12
 
10.6%
116119000000 1
 
0.9%
360319000 1
 
0.9%
19422000 1
 
0.9%
170448000 1
 
0.9%
1627861000 1
 
0.9%
15740000 1
 
0.9%
20566915000 1
 
0.9%
587157000 1
 
0.9%
7607000 1
 
0.9%
Other values (81) 81
71.7%
(Missing) 11
 
9.7%
ValueCountFrequency (%)
0 12
10.6%
2900000 1
 
0.9%
3050000 1
 
0.9%
3900000 1
 
0.9%
4300000 1
 
0.9%
5137000 1
 
0.9%
5715000 1
 
0.9%
5794000 1
 
0.9%
7325000 1
 
0.9%
7607000 1
 
0.9%
ValueCountFrequency (%)
127692000000 1
0.9%
116119000000 1
0.9%
99912110000 1
0.9%
93627506000 1
0.9%
89391334000 1
0.9%
43012452000 1
0.9%
38656043000 1
0.9%
31418606000 1
0.9%
28559900000 1
0.9%
20566915000 1
0.9%

감면금액
Real number (ℝ)

HIGH CORRELATION 

Distinct109
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0686917 × 109
Minimum1000
Maximum2.9220336 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T20:12:58.465863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile6800
Q144867000
median3.61256 × 108
Q34.2534 × 109
95-th percentile2.0366905 × 1010
Maximum2.9220336 × 1010
Range2.9220335 × 1010
Interquartile range (IQR)4.208533 × 109

Descriptive statistics

Standard deviation7.1446076 × 109
Coefficient of variation (CV)1.7559963
Kurtosis1.9111449
Mean4.0686917 × 109
Median Absolute Deviation (MAD)3.59408 × 108
Skewness1.7671101
Sum4.5976216 × 1011
Variance5.1045417 × 1019
MonotonicityNot monotonic
2023-12-12T20:12:58.774949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 3
 
2.7%
8000 2
 
1.8%
3000 2
 
1.8%
44503000 1
 
0.9%
110184000 1
 
0.9%
77164000 1
 
0.9%
581077000 1
 
0.9%
10282203000 1
 
0.9%
64148000 1
 
0.9%
5028396000 1
 
0.9%
Other values (99) 99
87.6%
ValueCountFrequency (%)
1000 3
2.7%
3000 2
1.8%
5000 1
 
0.9%
8000 2
1.8%
10000 1
 
0.9%
12000 1
 
0.9%
23000 1
 
0.9%
34000 1
 
0.9%
58000 1
 
0.9%
922000 1
 
0.9%
ValueCountFrequency (%)
29220336000 1
0.9%
24744559000 1
0.9%
23004293000 1
0.9%
21279678000 1
0.9%
20932619000 1
0.9%
20441081000 1
0.9%
20317455000 1
0.9%
19849090000 1
0.9%
19735346000 1
0.9%
19279618000 1
0.9%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct103
Distinct (%)91.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5943512 × 1010
Minimum0
Maximum7.09718 × 1011
Zeros11
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T20:12:59.061580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.879408 × 109
median2.8431739 × 1010
Q37.6729188 × 1010
95-th percentile2.957074 × 1011
Maximum7.09718 × 1011
Range7.09718 × 1011
Interquartile range (IQR)7.084978 × 1010

Descriptive statistics

Standard deviation1.1068379 × 1011
Coefficient of variation (CV)1.6784637
Kurtosis13.407545
Mean6.5943512 × 1010
Median Absolute Deviation (MAD)2.3387531 × 1010
Skewness3.3458688
Sum7.4516169 × 1012
Variance1.2250901 × 1022
MonotonicityNot monotonic
2023-12-12T20:12:59.342088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11
 
9.7%
47305271000 1
 
0.9%
303613000000 1
 
0.9%
126838000000 1
 
0.9%
5324275000 1
 
0.9%
6739647000 1
 
0.9%
28431739000 1
 
0.9%
108283000000 1
 
0.9%
7732303000 1
 
0.9%
41532763000 1
 
0.9%
Other values (93) 93
82.3%
ValueCountFrequency (%)
0 11
9.7%
2441597000 1
 
0.9%
2588303000 1
 
0.9%
3048927000 1
 
0.9%
3385410000 1
 
0.9%
4210534000 1
 
0.9%
4720726000 1
 
0.9%
4821646000 1
 
0.9%
4979693000 1
 
0.9%
5009728000 1
 
0.9%
ValueCountFrequency (%)
709718000000 1
0.9%
514679000000 1
0.9%
489597000000 1
0.9%
334083000000 1
0.9%
322035000000 1
0.9%
303613000000 1
0.9%
290437000000 1
0.9%
230106000000 1
0.9%
223691000000 1
0.9%
205075000000 1
0.9%

비과세감면율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)40.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.72885
Minimum0
Maximum69
Zeros27
Zeros (%)23.9%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-12T20:12:59.642520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.68
median3
Q312
95-th percentile60
Maximum69
Range69
Interquartile range (IQR)11.32

Descriptive statistics

Standard deviation19.14947
Coefficient of variation (CV)1.632681
Kurtosis2.3401592
Mean11.72885
Median Absolute Deviation (MAD)3
Skewness1.8980793
Sum1325.36
Variance366.7022
MonotonicityNot monotonic
2023-12-12T20:12:59.859607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0.0 27
23.9%
1.0 17
15.0%
3.0 12
 
10.6%
23.0 3
 
2.7%
7.0 3
 
2.7%
11.0 3
 
2.7%
6.0 2
 
1.8%
56.0 2
 
1.8%
69.0 2
 
1.8%
4.0 2
 
1.8%
Other values (36) 40
35.4%
ValueCountFrequency (%)
0.0 27
23.9%
0.35 1
 
0.9%
0.68 1
 
0.9%
0.82 1
 
0.9%
0.97 1
 
0.9%
1.0 17
15.0%
1.26 1
 
0.9%
1.46 1
 
0.9%
1.47 1
 
0.9%
2.0 2
 
1.8%
ValueCountFrequency (%)
69.0 2
1.8%
66.0 1
0.9%
64.78 1
0.9%
62.0 1
0.9%
60.0 2
1.8%
58.0 1
0.9%
56.0 2
1.8%
54.0 1
0.9%
52.0 1
0.9%
45.11 1
0.9%

Interactions

2023-12-12T20:12:54.625054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:52.639470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:53.355059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:54.005540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:54.775635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:52.827629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:53.524662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:54.158496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:54.923506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:53.004103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:53.676298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:54.341362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:55.071630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:53.169399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:53.830124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:12:54.478924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:13:00.028630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명자치단체코드세목명과세년도비과세금액감면금액부과금액비과세감면율
시군구명1.0001.0000.0000.0000.4830.2660.3910.468
자치단체코드1.0001.0000.0000.0000.4830.2660.3910.468
세목명0.0000.0001.0000.0000.5200.5840.5290.710
과세년도0.0000.0000.0001.0000.0000.0000.0000.000
비과세금액0.4830.4830.5200.0001.0000.7690.6810.917
감면금액0.2660.2660.5840.0000.7691.0000.7770.924
부과금액0.3910.3910.5290.0000.6810.7771.0000.622
비과세감면율0.4680.4680.7100.0000.9170.9240.6221.000
2023-12-12T20:13:00.196904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도시군구명세목명자치단체코드
과세년도1.0000.0000.0000.000
시군구명0.0001.0000.0001.000
세목명0.0000.0001.0000.000
자치단체코드0.0001.0000.0001.000
2023-12-12T20:13:00.340713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비과세금액감면금액부과금액비과세감면율시군구명자치단체코드세목명과세년도
비과세금액1.0000.8660.4170.9130.2230.2230.3400.000
감면금액0.8661.0000.5900.7950.1550.1550.3260.000
부과금액0.4170.5901.0000.3650.2630.2630.1990.000
비과세감면율0.9130.7950.3651.0000.3070.3070.4350.000
시군구명0.2230.1550.2630.3071.0001.0000.0000.000
자치단체코드0.2230.1550.2630.3071.0001.0000.0000.000
세목명0.3400.3260.1990.4350.0000.0001.0000.000
과세년도0.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-12T20:12:55.250154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:12:55.466696image/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

시도명시군구명자치단체코드세목명과세년도비과세금액감면금액부과금액비과세감면율
0경기도성남시수정구41131등록세2017<NA>5800000.0
1경기도성남시수정구41131재산세20171843598600078653250004730527100056.0
2경기도성남시수정구41131주민세2017194760004395200024415970003.0
3경기도성남시수정구41131취득세201742880540002474455900019315800000015.0
4경기도성남시수정구41131자동차세2017150847000710508000280314710003.0
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