Overview

Dataset statistics

Number of variables9
Number of observations34
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.7 KiB
Average record size in memory81.9 B

Variable types

Categorical5
Numeric4

Dataset

Description연도별 지방세 과세 및 비과세 현황을 세목별로 제공(시도명,시군구명,자치단체코드,과세년도,세목명,과세건수,과세금액,비과세건수,비과세금액)
URLhttps://www.data.go.kr/data/15079138/fileData.do

Alerts

시도명 has constant value ""Constant
과세년도 has constant value ""Constant
자치단체코드 is highly overall correlated with 시군구명High correlation
시군구명 is highly overall correlated with 자치단체코드High correlation
비과세건수 is highly overall correlated with 비과세금액High correlation
비과세금액 is highly overall correlated with 비과세건수High correlation
과세건수 has 4 (11.8%) zerosZeros
과세금액 has 4 (11.8%) zerosZeros
비과세건수 has 12 (35.3%) zerosZeros
비과세금액 has 12 (35.3%) zerosZeros

Reproduction

Analysis started2023-12-12 22:29:34.690985
Analysis finished2023-12-12 22:29:36.141984
Duration1.45 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size404.0 B
경기도
34 

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 34
100.0%

Length

2023-12-13T07:29:36.189445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:29:36.260183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 34
100.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Memory size404.0 B
고양시일산동구
13 
고양시덕양구
8 
고양시일산서구
8 
고양시
5 

Length

Max length7
Median length7
Mean length6.1764706
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
고양시일산동구 13
38.2%
고양시덕양구 8
23.5%
고양시일산서구 8
23.5%
고양시 5
 
14.7%

Length

2023-12-13T07:29:36.344710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:29:36.430612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
고양시일산동구 13
38.2%
고양시덕양구 8
23.5%
고양시일산서구 8
23.5%
고양시 5
 
14.7%

자치단체코드
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Memory size404.0 B
41285
13 
41281
8 
41287
8 
41280
5 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row41280
2nd row41280
3rd row41280
4th row41280
5th row41280

Common Values

ValueCountFrequency (%)
41285 13
38.2%
41281 8
23.5%
41287 8
23.5%
41280 5
 
14.7%

Length

2023-12-13T07:29:36.518667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:29:36.596150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
41285 13
38.2%
41281 8
23.5%
41287 8
23.5%
41280 5
 
14.7%

과세년도
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size404.0 B
2022
34 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 34
100.0%

Length

2023-12-13T07:29:36.682806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:29:36.752844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 34
100.0%

세목명
Categorical

Distinct13
Distinct (%)38.2%
Missing0
Missing (%)0.0%
Memory size404.0 B
교육세
4 
자동차세
4 
지역자원시설세
3 
지방소득세
3 
등록면허세
3 
Other values (8)
17 

Length

Max length7
Median length5
Mean length4.1176471
Min length3

Unique

Unique2 ?
Unique (%)5.9%

Sample

1st row레저세
2nd row담배소비세
3rd row교육세
4th row지방소비세
5th row자동차세

Common Values

ValueCountFrequency (%)
교육세 4
11.8%
자동차세 4
11.8%
지역자원시설세 3
8.8%
지방소득세 3
8.8%
등록면허세 3
8.8%
재산세 3
8.8%
취득세 3
8.8%
주민세 3
8.8%
레저세 2
5.9%
담배소비세 2
5.9%
Other values (3) 4
11.8%

Length

2023-12-13T07:29:36.839507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
교육세 4
11.8%
자동차세 4
11.8%
지역자원시설세 3
8.8%
지방소득세 3
8.8%
등록면허세 3
8.8%
재산세 3
8.8%
취득세 3
8.8%
주민세 3
8.8%
레저세 2
5.9%
담배소비세 2
5.9%
Other values (3) 4
11.8%

과세건수
Real number (ℝ)

ZEROS 

Distinct30
Distinct (%)88.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174022.35
Minimum0
Maximum983428
Zeros4
Zeros (%)11.8%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-13T07:29:36.945505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1649.75
median147400.5
Q3232452.75
95-th percentile614389.7
Maximum983428
Range983428
Interquartile range (IQR)231803

Descriptive statistics

Standard deviation213409.04
Coefficient of variation (CV)1.2263312
Kurtosis5.911482
Mean174022.35
Median Absolute Deviation (MAD)117187.5
Skewness2.1929752
Sum5916760
Variance4.5543419 × 1010
MonotonicityNot monotonic
2023-12-13T07:29:37.047586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 4
 
11.8%
12 2
 
5.9%
45 1
 
2.9%
656927 1
 
2.9%
591485 1
 
2.9%
237606 1
 
2.9%
152864 1
 
2.9%
24335 1
 
2.9%
125375 1
 
2.9%
160745 1
 
2.9%
Other values (20) 20
58.8%
ValueCountFrequency (%)
0 4
11.8%
9 1
 
2.9%
12 2
5.9%
45 1
 
2.9%
639 1
 
2.9%
682 1
 
2.9%
24335 1
 
2.9%
34563 1
 
2.9%
59532 1
 
2.9%
83091 1
 
2.9%
ValueCountFrequency (%)
983428 1
2.9%
656927 1
2.9%
591485 1
2.9%
333112 1
2.9%
292693 1
2.9%
268938 1
2.9%
243417 1
2.9%
239519 1
2.9%
237606 1
2.9%
216993 1
2.9%

과세금액
Real number (ℝ)

ZEROS 

Distinct31
Distinct (%)91.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6940777 × 1010
Minimum0
Maximum3.2298574 × 1011
Zeros4
Zeros (%)11.8%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-13T07:29:37.143826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18.9893112 × 109
median2.4991537 × 1010
Q36.3341565 × 1010
95-th percentile1.3818303 × 1011
Maximum3.2298574 × 1011
Range3.2298574 × 1011
Interquartile range (IQR)5.4352254 × 1010

Descriptive statistics

Standard deviation6.4032251 × 1010
Coefficient of variation (CV)1.3641072
Kurtosis9.9063094
Mean4.6940777 × 1010
Median Absolute Deviation (MAD)1.8848718 × 1010
Skewness2.778824
Sum1.5959864 × 1012
Variance4.1001292 × 1021
MonotonicityNot monotonic
2023-12-13T07:29:37.242942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 4
 
11.8%
582962000 1
 
2.9%
39482088000 1
 
2.9%
21911334000 1
 
2.9%
6791292000 1
 
2.9%
64033723000 1
 
2.9%
85084052000 1
 
2.9%
5494347000 1
 
2.9%
64239159000 1
 
2.9%
25166866000 1
 
2.9%
Other values (21) 21
61.8%
ValueCountFrequency (%)
0 4
11.8%
582962000 1
 
2.9%
5494347000 1
 
2.9%
6791292000 1
 
2.9%
7692831000 1
 
2.9%
8732263000 1
 
2.9%
9760456000 1
 
2.9%
10914250000 1
 
2.9%
11747340000 1
 
2.9%
12083600000 1
 
2.9%
ValueCountFrequency (%)
322985739000 1
2.9%
160816186000 1
2.9%
125995940000 1
2.9%
122605797000 1
2.9%
97613057000 1
2.9%
92988434000 1
2.9%
85084052000 1
2.9%
64239159000 1
2.9%
64033723000 1
2.9%
61265090000 1
2.9%

비과세건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)67.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12951.882
Minimum0
Maximum94226
Zeros12
Zeros (%)35.3%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-13T07:29:37.356576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1146
Q314807.25
95-th percentile54492.35
Maximum94226
Range94226
Interquartile range (IQR)14807.25

Descriptive statistics

Standard deviation21323.748
Coefficient of variation (CV)1.6463822
Kurtosis5.8265794
Mean12951.882
Median Absolute Deviation (MAD)1146
Skewness2.2979332
Sum440364
Variance4.5470225 × 108
MonotonicityNot monotonic
2023-12-13T07:29:37.450904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 12
35.3%
2757 1
 
2.9%
36 1
 
2.9%
859 1
 
2.9%
12063 1
 
2.9%
14854 1
 
2.9%
51895 1
 
2.9%
33284 1
 
2.9%
7686 1
 
2.9%
31943 1
 
2.9%
Other values (13) 13
38.2%
ValueCountFrequency (%)
0 12
35.3%
24 1
 
2.9%
27 1
 
2.9%
36 1
 
2.9%
87 1
 
2.9%
859 1
 
2.9%
1433 1
 
2.9%
2757 1
 
2.9%
7686 1
 
2.9%
10188 1
 
2.9%
ValueCountFrequency (%)
94226 1
2.9%
59316 1
2.9%
51895 1
2.9%
39532 1
2.9%
33284 1
2.9%
31943 1
2.9%
24541 1
2.9%
16179 1
2.9%
14854 1
2.9%
14667 1
2.9%

비과세금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)67.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.6653034 × 109
Minimum0
Maximum8.3041424 × 1010
Zeros12
Zeros (%)35.3%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-13T07:29:37.538135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median81363500
Q31.1503352 × 109
95-th percentile5.644505 × 1010
Maximum8.3041424 × 1010
Range8.3041424 × 1010
Interquartile range (IQR)1.1503352 × 109

Descriptive statistics

Standard deviation2.1032434 × 1010
Coefficient of variation (CV)2.4272011
Kurtosis6.2595689
Mean8.6653034 × 109
Median Absolute Deviation (MAD)81363500
Skewness2.6396516
Sum2.9462032 × 1011
Variance4.4236327 × 1020
MonotonicityNot monotonic
2023-12-13T07:29:37.636714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 12
35.3%
1026298000 1
 
2.9%
10000 1
 
2.9%
797852000 1
 
2.9%
19334775000 1
 
2.9%
41171000 1
 
2.9%
43771506000 1
 
2.9%
1320679000 1
 
2.9%
295623000 1
 
2.9%
1191681000 1
 
2.9%
Other values (13) 13
38.2%
ValueCountFrequency (%)
0 12
35.3%
7000 1
 
2.9%
10000 1
 
2.9%
23000 1
 
2.9%
41171000 1
 
2.9%
59541000 1
 
2.9%
103186000 1
 
2.9%
164564000 1
 
2.9%
226068000 1
 
2.9%
295623000 1
 
2.9%
ValueCountFrequency (%)
83041424000 1
2.9%
72702560000 1
2.9%
47691006000 1
2.9%
43771506000 1
2.9%
19911582000 1
2.9%
19334775000 1
2.9%
1685166000 1
2.9%
1320679000 1
2.9%
1191681000 1
2.9%
1026298000 1
2.9%

Interactions

2023-12-13T07:29:35.732538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:34.940304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:35.188125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:35.454978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:35.797107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:35.002387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:35.254281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:35.528843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:35.862203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:35.068258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:35.320282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:35.608251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:35.918867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:35.126941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:35.382545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:29:35.675735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:29:37.720470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명자치단체코드세목명과세건수과세금액비과세건수비과세금액
시군구명1.0001.0000.0000.4280.0000.0000.000
자치단체코드1.0001.0000.0000.4280.0000.0000.000
세목명0.0000.0001.0000.5260.3670.5380.701
과세건수0.4280.4280.5261.0000.0000.0000.000
과세금액0.0000.0000.3670.0001.0000.3720.807
비과세건수0.0000.0000.5380.0000.3721.0000.825
비과세금액0.0000.0000.7010.0000.8070.8251.000
2023-12-13T07:29:38.126935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자치단체코드시군구명세목명
자치단체코드1.0001.0000.000
시군구명1.0001.0000.000
세목명0.0000.0001.000
2023-12-13T07:29:38.214313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세건수과세금액비과세건수비과세금액시군구명자치단체코드세목명
과세건수1.0000.3980.4370.3860.2710.2710.230
과세금액0.3981.0000.2530.3690.0000.0000.124
비과세건수0.4370.2531.0000.9000.0000.0000.224
비과세금액0.3860.3690.9001.0000.0000.0000.394
시군구명0.2710.0000.0000.0001.0001.0000.000
자치단체코드0.2710.0000.0000.0001.0001.0000.000
세목명0.2300.1240.2240.3940.0000.0001.000

Missing values

2023-12-13T07:29:36.002738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:29:36.103087image/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

시도명시군구명자치단체코드과세년도세목명과세건수과세금액비과세건수비과세금액
0경기도고양시412802022레저세4558296200000
1경기도고양시412802022담배소비세6396126509000000
2경기도고양시412802022교육세6822718369700000
3경기도고양시412802022지방소비세92481620800000
4경기도고양시412802022자동차세123824918800000
5경기도고양시덕양구412812022지역자원시설세3331121208360000027571026298000
6경기도고양시덕양구412812022지방소득세2434179298843400000
7경기도고양시덕양구412812022등록면허세1484221499347100013494431613000
8경기도고양시덕양구412812022자동차세29269338379368000395321685166000
9경기도고양시덕양구412812022재산세2689381259959400009422672702560000
시도명시군구명자치단체코드과세년도세목명과세건수과세금액비과세건수비과세금액
24경기도고양시일산동구412852022자동차세20909728396781000319431191681000
25경기도고양시일산동구412852022레저세121174734000000
26경기도고양시일산서구412872022등록면허세8309176928310007686295623000
27경기도고양시일산서구412872022자동차세18382025166866000332841320679000
28경기도고양시일산서구412872022재산세160745642391590005189543771506000
29경기도고양시일산서구412872022주민세12537554943470001485441171000
30경기도고양시일산서구412872022취득세24335850840520001206319334775000
31경기도고양시일산서구412872022지방소득세1528646403372300000
32경기도고양시일산서구412872022지역자원시설세2376066791292000859797852000
33경기도고양시일산서구412872022교육세591485219113340003610000