Overview

Dataset statistics

Number of variables9
Number of observations23
Missing cells3
Missing cells (%)1.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 KiB
Average record size in memory83.7 B

Variable types

Categorical5
Numeric4

Dataset

Description경기도 포천시의 세목별 과세액 중 비과세액과 감면액이 차지하는 비율 현황을 제공함으로써 국민 조세 혜택 규모를 파악하는 데 사용할 수 있도록 함.
Author경기도 포천시
URLhttps://www.data.go.kr/data/15080061/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 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 3 other fieldsHigh correlation
비과세감면율 is highly overall correlated with 비과세금액 and 2 other fieldsHigh correlation
세목명 is highly overall correlated with 비과세금액 and 2 other fieldsHigh correlation
비과세금액 has 3 (13.0%) missing valuesMissing
감면금액 has unique valuesUnique
비과세금액 has 2 (8.7%) zerosZeros
부과금액 has 3 (13.0%) zerosZeros
비과세감면율 has 5 (21.7%) zerosZeros

Reproduction

Analysis started2023-12-12 14:25:18.612343
Analysis finished2023-12-12 14:25:20.608399
Duration2 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size316.0 B
경기도
23 

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 23
100.0%

Length

2023-12-12T23:25:20.670424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:25:20.751608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 23
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size316.0 B
포천시
23 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
포천시 23
100.0%

Length

2023-12-12T23:25:20.830609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:25:20.906150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
포천시 23
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size316.0 B
41650
23 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row41650
2nd row41650
3rd row41650
4th row41650
5th row41650

Common Values

ValueCountFrequency (%)
41650 23
100.0%

Length

2023-12-12T23:25:20.987833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:25:21.082166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
41650 23
100.0%

세목명
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)34.8%
Missing0
Missing (%)0.0%
Memory size316.0 B
등록세
3 
재산세
3 
주민세
3 
취득세
3 
자동차세
3 
Other values (3)
8 

Length

Max length7
Median length3
Mean length3.9130435
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
등록세 3
13.0%
재산세 3
13.0%
주민세 3
13.0%
취득세 3
13.0%
자동차세 3
13.0%
등록면허세 3
13.0%
지역자원시설세 3
13.0%
교육세 2
8.7%

Length

2023-12-12T23:25:21.198140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:25:21.316891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
등록세 3
13.0%
재산세 3
13.0%
주민세 3
13.0%
취득세 3
13.0%
자동차세 3
13.0%
등록면허세 3
13.0%
지역자원시설세 3
13.0%
교육세 2
8.7%

과세년도
Categorical

Distinct3
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2018
8 
2019
8 
2017
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 (%)
2018 8
34.8%
2019 8
34.8%
2017 7
30.4%

Length

2023-12-12T23:25:21.443549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:25:21.530351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 8
34.8%
2019 8
34.8%
2017 7
30.4%

비과세금액
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct19
Distinct (%)95.0%
Missing3
Missing (%)13.0%
Infinite0
Infinite (%)0.0%
Mean2.3968574 × 109
Minimum0
Maximum1.3086821 × 1010
Zeros2
Zeros (%)8.7%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T23:25:21.627379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q130821000
median3.9047 × 108
Q32.196617 × 109
95-th percentile1.2443817 × 1010
Maximum1.3086821 × 1010
Range1.3086821 × 1010
Interquartile range (IQR)2.165796 × 109

Descriptive statistics

Standard deviation4.4615137 × 109
Coefficient of variation (CV)1.8614014
Kurtosis2.3137764
Mean2.3968574 × 109
Median Absolute Deviation (MAD)3.870515 × 108
Skewness1.9522029
Sum4.7937148 × 1010
Variance1.9905104 × 1019
MonotonicityNot monotonic
2023-12-12T23:25:21.756215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 2
 
8.7%
3644000 1
 
4.3%
412756000 1
 
4.3%
3193000 1
 
4.3%
233140000 1
 
4.3%
2048947000 1
 
4.3%
45860000 1
 
4.3%
12409975000 1
 
4.3%
404065000 1
 
4.3%
13086821000 1
 
4.3%
Other values (9) 9
39.1%
(Missing) 3
 
13.0%
ValueCountFrequency (%)
0 2
8.7%
798000 1
4.3%
3193000 1
4.3%
3644000 1
4.3%
39880000 1
4.3%
40280000 1
4.3%
45860000 1
4.3%
233140000 1
4.3%
386708000 1
4.3%
394232000 1
4.3%
ValueCountFrequency (%)
13086821000 1
4.3%
12409975000 1
4.3%
12036089000 1
4.3%
3299659000 1
4.3%
2639627000 1
4.3%
2048947000 1
4.3%
451474000 1
4.3%
412756000 1
4.3%
404065000 1
4.3%
394232000 1
4.3%

감면금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1946709 × 109
Minimum1000
Maximum1.4007164 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T23:25:21.880611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile29100
Q129134500
median2.24466 × 108
Q31.597307 × 109
95-th percentile1.3600847 × 1010
Maximum1.4007164 × 1010
Range1.4007163 × 1010
Interquartile range (IQR)1.5681725 × 109

Descriptive statistics

Standard deviation4.4366997 × 109
Coefficient of variation (CV)2.0215786
Kurtosis3.6886314
Mean2.1946709 × 109
Median Absolute Deviation (MAD)2.24202 × 108
Skewness2.2554389
Sum5.047743 × 1010
Variance1.9684305 × 1019
MonotonicityNot monotonic
2023-12-12T23:25:22.003925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1296000 1
 
4.3%
2226483000 1
 
4.3%
184142000 1
 
4.3%
281632000 1
 
4.3%
968131000 1
 
4.3%
14007164000 1
 
4.3%
30801000 1
 
4.3%
2399558000 1
 
4.3%
264000 1
 
4.3%
3000 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
1000 1
4.3%
3000 1
4.3%
264000 1
4.3%
1296000 1
4.3%
8755000 1
4.3%
27468000 1
4.3%
30801000 1
4.3%
32369000 1
4.3%
184142000 1
4.3%
216761000 1
4.3%
ValueCountFrequency (%)
14007164000 1
4.3%
13803363000 1
4.3%
11778206000 1
4.3%
2399558000 1
4.3%
2302781000 1
4.3%
2226483000 1
4.3%
968131000 1
4.3%
745710000 1
4.3%
715939000 1
4.3%
298907000 1
4.3%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5010944 × 1010
Minimum0
Maximum8.1698195 × 1010
Zeros3
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T23:25:22.133446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.429399 × 109
median8.515046 × 109
Q33.7801281 × 1010
95-th percentile7.7300873 × 1010
Maximum8.1698195 × 1010
Range8.1698195 × 1010
Interquartile range (IQR)3.1371882 × 1010

Descriptive statistics

Standard deviation2.5504448 × 1010
Coefficient of variation (CV)1.0197315
Kurtosis0.23878605
Mean2.5010944 × 1010
Median Absolute Deviation (MAD)8.515046 × 109
Skewness1.0886483
Sum5.752517 × 1011
Variance6.5047685 × 1020
MonotonicityNot monotonic
2023-12-12T23:25:22.255752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 3
 
13.0%
36975331000 1
 
4.3%
7906295000 1
 
4.3%
7994913000 1
 
4.3%
31313398000 1
 
4.3%
73947770000 1
 
4.3%
5755638000 1
 
4.3%
40861483000 1
 
4.3%
25038676000 1
 
4.3%
7947361000 1
 
4.3%
Other values (11) 11
47.8%
ValueCountFrequency (%)
0 3
13.0%
5239756000 1
 
4.3%
5423509000 1
 
4.3%
5755638000 1
 
4.3%
7103160000 1
 
4.3%
7294444000 1
 
4.3%
7906295000 1
 
4.3%
7947361000 1
 
4.3%
7994913000 1
 
4.3%
8515046000 1
 
4.3%
ValueCountFrequency (%)
81698195000 1
4.3%
77673440000 1
4.3%
73947770000 1
4.3%
44655221000 1
4.3%
40861483000 1
4.3%
38627231000 1
4.3%
36975331000 1
4.3%
36033464000 1
4.3%
31313398000 1
4.3%
25247373000 1
4.3%

비과세감면율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)82.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.736087
Minimum0
Maximum41.41
Zeros5
Zeros (%)21.7%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T23:25:22.373462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.31
median3.56
Q313.005
95-th percentile37.032
Maximum41.41
Range41.41
Interquartile range (IQR)11.695

Descriptive statistics

Standard deviation13.068299
Coefficient of variation (CV)1.3422537
Kurtosis1.0084447
Mean9.736087
Median Absolute Deviation (MAD)3.56
Skewness1.4935077
Sum223.93
Variance170.78044
MonotonicityNot monotonic
2023-12-12T23:25:22.492020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0.0 5
21.7%
41.41 1
 
4.3%
7.55 1
 
4.3%
3.56 1
 
4.3%
3.84 1
 
4.3%
21.71 1
 
4.3%
1.33 1
 
4.3%
36.24 1
 
4.3%
7.91 1
 
4.3%
2.59 1
 
4.3%
Other values (9) 9
39.1%
ValueCountFrequency (%)
0.0 5
21.7%
1.29 1
 
4.3%
1.33 1
 
4.3%
1.34 1
 
4.3%
2.55 1
 
4.3%
2.59 1
 
4.3%
3.24 1
 
4.3%
3.56 1
 
4.3%
3.84 1
 
4.3%
4.22 1
 
4.3%
ValueCountFrequency (%)
41.41 1
4.3%
37.12 1
4.3%
36.24 1
4.3%
22.02 1
4.3%
21.71 1
4.3%
17.65 1
4.3%
8.36 1
4.3%
7.91 1
4.3%
7.55 1
4.3%
4.22 1
4.3%

Interactions

2023-12-12T23:25:20.059866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:18.879593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:19.344524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:19.726636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:20.148931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:18.984789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:19.452039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:19.809580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:20.231018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:19.106357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:19.550858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:19.897602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:20.314374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:19.231317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:19.652660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:19.982675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:25:22.591566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도비과세금액감면금액부과금액비과세감면율
세목명1.0000.0000.8170.9530.8200.719
과세년도0.0001.0000.0000.0000.0000.000
비과세금액0.8170.0001.0000.9860.7500.867
감면금액0.9530.0000.9861.0000.7671.000
부과금액0.8200.0000.7500.7671.0000.560
비과세감면율0.7190.0000.8671.0000.5601.000
2023-12-12T23:25:22.696094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도
세목명1.0000.000
과세년도0.0001.000
2023-12-12T23:25:22.811793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비과세금액감면금액부과금액비과세감면율세목명과세년도
비과세금액1.0000.7780.6250.8450.6450.000
감면금액0.7781.0000.7920.8790.6350.000
부과금액0.6250.7921.0000.6600.5910.000
비과세감면율0.8450.8790.6601.0000.4610.000
세목명0.6450.6350.5910.4611.0000.000
과세년도0.0000.0000.0000.0000.0001.000

Missing values

2023-12-12T23:25:20.413665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:25:20.545097image/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
0경기도포천시41650등록세2017<NA>129600000.0
1경기도포천시41650재산세20171308682100022264830003697533100041.41
2경기도포천시41650주민세2017398800002746800052397560001.29
3경기도포천시41650취득세20172639627000117782060008169819500017.65
4경기도포천시41650자동차세2017394232000745710000446552210002.55
5경기도포천시41650등록면허세201779800029890700071031600004.22
6경기도포천시41650지역자원시설세201738670800022323000072944440008.36
7경기도포천시41650교육세201801000252473730000.0
8경기도포천시41650등록세2018<NA>875500000.0
9경기도포천시41650재산세20181203608900023027810003862723100037.12
시도명시군구명자치단체코드세목명과세년도비과세금액감면금액부과금액비과세감면율
13경기도포천시41650등록면허세2018364400021676100085150460002.59
14경기도포천시41650지역자원시설세201840406500022446600079473610007.91
15경기도포천시41650교육세201903000250386760000.0
16경기도포천시41650등록세2019<NA>26400000.0
17경기도포천시41650재산세20191240997500023995580004086148300036.24
18경기도포천시41650주민세2019458600003080100057556380001.33
19경기도포천시41650취득세20192048947000140071640007394777000021.71
20경기도포천시41650자동차세2019233140000968131000313133980003.84
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