Overview

Dataset statistics

Number of variables9
Number of observations64
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.0 KiB
Average record size in memory80.1 B

Variable types

Categorical5
Numeric4

Dataset

Description대전광역시 중구의 각 세목별로 과세건수와 과세금액을 알 수 있으며, 비과세건수와 비과세금액에 대해서도 확인할 수 있습니다.
URLhttps://www.data.go.kr/data/15078534/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
과세건수 is highly overall correlated with 과세금액 and 2 other fieldsHigh correlation
과세금액 is highly overall correlated with 과세건수 and 2 other fieldsHigh correlation
비과세건수 is highly overall correlated with 과세건수 and 3 other fieldsHigh correlation
비과세금액 is highly overall correlated with 과세금액 and 1 other fieldsHigh correlation
세목명 is highly overall correlated with 과세건수 and 1 other fieldsHigh correlation
과세건수 has 22 (34.4%) zerosZeros
과세금액 has 23 (35.9%) zerosZeros
비과세건수 has 25 (39.1%) zerosZeros
비과세금액 has 29 (45.3%) zerosZeros

Reproduction

Analysis started2023-12-12 17:47:43.562190
Analysis finished2023-12-12 17:47:45.789583
Duration2.23 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size644.0 B
대전광역시
64 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대전광역시 64
100.0%

Length

2023-12-13T02:47:45.892762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:47:46.004380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대전광역시 64
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size644.0 B
중구
64 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
중구 64
100.0%

Length

2023-12-13T02:47:46.143543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:47:46.251365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
중구 64
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size644.0 B
30140
64 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30140
2nd row30140
3rd row30140
4th row30140
5th row30140

Common Values

ValueCountFrequency (%)
30140 64
100.0%

Length

2023-12-13T02:47:46.356821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:47:46.457042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
30140 64
100.0%

과세연도
Categorical

Distinct5
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Memory size644.0 B
2017
13 
2018
13 
2020
13 
2021
13 
2019
12 

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 13
20.3%
2018 13
20.3%
2020 13
20.3%
2021 13
20.3%
2019 12
18.8%

Length

2023-12-13T02:47:46.563667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:47:46.668714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 13
20.3%
2018 13
20.3%
2020 13
20.3%
2021 13
20.3%
2019 12
18.8%

세목명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)20.3%
Missing0
Missing (%)0.0%
Memory size644.0 B
취득세
5 
주민세
5 
재산세
5 
자동차세
5 
레저세
5 
Other values (8)
39 

Length

Max length7
Median length5
Mean length4.171875
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
취득세 5
 
7.8%
주민세 5
 
7.8%
재산세 5
 
7.8%
자동차세 5
 
7.8%
레저세 5
 
7.8%
담배소비세 5
 
7.8%
지방소비세 5
 
7.8%
등록면허세 5
 
7.8%
도시계획세 5
 
7.8%
지역자원시설세 5
 
7.8%
Other values (3) 14
21.9%

Length

2023-12-13T02:47:46.792008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
취득세 5
 
7.8%
주민세 5
 
7.8%
재산세 5
 
7.8%
자동차세 5
 
7.8%
레저세 5
 
7.8%
담배소비세 5
 
7.8%
지방소비세 5
 
7.8%
등록면허세 5
 
7.8%
도시계획세 5
 
7.8%
지역자원시설세 5
 
7.8%
Other values (3) 14
21.9%

과세건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct43
Distinct (%)67.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86234.156
Minimum0
Maximum452896
Zeros22
Zeros (%)34.4%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-13T02:47:46.909623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median65169
Q3122698.25
95-th percentile442007.3
Maximum452896
Range452896
Interquartile range (IQR)122698.25

Descriptive statistics

Standard deviation119293.08
Coefficient of variation (CV)1.3833623
Kurtosis4.125451
Mean86234.156
Median Absolute Deviation (MAD)65166
Skewness2.0786921
Sum5518986
Variance1.4230839 × 1010
MonotonicityNot monotonic
2023-12-13T02:47:47.045356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0 22
34.4%
9558 1
 
1.6%
445640 1
 
1.6%
11493 1
 
1.6%
106406 1
 
1.6%
126385 1
 
1.6%
158759 1
 
1.6%
6 1
 
1.6%
74566 1
 
1.6%
128109 1
 
1.6%
Other values (33) 33
51.6%
ValueCountFrequency (%)
0 22
34.4%
6 1
 
1.6%
7 1
 
1.6%
9233 1
 
1.6%
9558 1
 
1.6%
10264 1
 
1.6%
10958 1
 
1.6%
11493 1
 
1.6%
55689 1
 
1.6%
60248 1
 
1.6%
ValueCountFrequency (%)
452896 1
1.6%
445640 1
1.6%
442588 1
1.6%
442256 1
1.6%
440598 1
1.6%
158937 1
1.6%
158759 1
1.6%
158023 1
1.6%
157253 1
1.6%
156384 1
1.6%

과세금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct42
Distinct (%)65.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3275677 × 1010
Minimum0
Maximum7.7150644 × 1010
Zeros23
Zeros (%)35.9%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-13T02:47:47.166610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5.357286 × 109
Q31.8719994 × 1010
95-th percentile4.2953322 × 1010
Maximum7.7150644 × 1010
Range7.7150644 × 1010
Interquartile range (IQR)1.8719994 × 1010

Descriptive statistics

Standard deviation1.6929425 × 1010
Coefficient of variation (CV)1.2752213
Kurtosis2.2361284
Mean1.3275677 × 1010
Median Absolute Deviation (MAD)5.357286 × 109
Skewness1.5196638
Sum8.4964333 × 1011
Variance2.8660545 × 1020
MonotonicityNot monotonic
2023-12-13T02:47:47.298760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 23
35.9%
4159209000 1
 
1.6%
35229461000 1
 
1.6%
14081077000 1
 
1.6%
56506306000 1
 
1.6%
6821179000 1
 
1.6%
32778111000 1
 
1.6%
18701664000 1
 
1.6%
5244631000 1
 
1.6%
4212136000 1
 
1.6%
Other values (32) 32
50.0%
ValueCountFrequency (%)
0 23
35.9%
3994593000 1
 
1.6%
4042975000 1
 
1.6%
4159209000 1
 
1.6%
4188084000 1
 
1.6%
4212136000 1
 
1.6%
4273069000 1
 
1.6%
4636294000 1
 
1.6%
5244631000 1
 
1.6%
5339829000 1
 
1.6%
ValueCountFrequency (%)
77150644000 1
1.6%
56506306000 1
1.6%
46335421000 1
1.6%
43311177000 1
1.6%
40925476000 1
1.6%
38309490000 1
1.6%
36635890000 1
1.6%
35229461000 1
1.6%
33538903000 1
1.6%
33450382000 1
1.6%

비과세건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)56.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5657.1719
Minimum0
Maximum29751
Zeros25
Zeros (%)39.1%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-13T02:47:47.432106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5.5
Q34757.75
95-th percentile26181.55
Maximum29751
Range29751
Interquartile range (IQR)4757.75

Descriptive statistics

Standard deviation9328.5196
Coefficient of variation (CV)1.6489723
Kurtosis0.98735584
Mean5657.1719
Median Absolute Deviation (MAD)5.5
Skewness1.5722276
Sum362059
Variance87021278
MonotonicityNot monotonic
2023-12-13T02:47:47.555370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 25
39.1%
5 3
 
4.7%
1 2
 
3.1%
2 2
 
3.1%
6 1
 
1.6%
4725 1
 
1.6%
11225 1
 
1.6%
29751 1
 
1.6%
25499 1
 
1.6%
2174 1
 
1.6%
Other values (26) 26
40.6%
ValueCountFrequency (%)
0 25
39.1%
1 2
 
3.1%
2 2
 
3.1%
5 3
 
4.7%
6 1
 
1.6%
9 1
 
1.6%
1346 1
 
1.6%
1376 1
 
1.6%
1412 1
 
1.6%
1567 1
 
1.6%
ValueCountFrequency (%)
29751 1
1.6%
29136 1
1.6%
29061 1
1.6%
26302 1
1.6%
25499 1
1.6%
25336 1
1.6%
24208 1
1.6%
23560 1
1.6%
20453 1
1.6%
19425 1
1.6%

비과세금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)56.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4101572 × 109
Minimum0
Maximum2.3327301 × 1010
Zeros29
Zeros (%)45.3%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-13T02:47:47.743727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median579500
Q31.2646338 × 109
95-th percentile1.9318475 × 1010
Maximum2.3327301 × 1010
Range2.3327301 × 1010
Interquartile range (IQR)1.2646338 × 109

Descriptive statistics

Standard deviation5.6688883 × 109
Coefficient of variation (CV)2.3520824
Kurtosis6.6555164
Mean2.4101572 × 109
Median Absolute Deviation (MAD)579500
Skewness2.7711923
Sum1.5425006 × 1011
Variance3.2136295 × 1019
MonotonicityNot monotonic
2023-12-13T02:47:47.869187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 29
45.3%
5376111000 1
 
1.6%
523960000 1
 
1.6%
526007000 1
 
1.6%
7569201000 1
 
1.6%
350000 1
 
1.6%
1677137000 1
 
1.6%
21348285000 1
 
1.6%
1223595000 1
 
1.6%
71576000 1
 
1.6%
Other values (26) 26
40.6%
ValueCountFrequency (%)
0 29
45.3%
1000 1
 
1.6%
8000 1
 
1.6%
350000 1
 
1.6%
809000 1
 
1.6%
986000 1
 
1.6%
54325000 1
 
1.6%
71576000 1
 
1.6%
72524000 1
 
1.6%
125940000 1
 
1.6%
ValueCountFrequency (%)
23327301000 1
1.6%
21348285000 1
1.6%
20311953000 1
1.6%
19444557000 1
1.6%
18604011000 1
1.6%
8759677000 1
1.6%
7569201000 1
1.6%
6465236000 1
1.6%
5743103000 1
1.6%
5376111000 1
1.6%

Interactions

2023-12-13T02:47:45.102263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:47:43.866958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:47:44.246896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:47:44.686432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:47:45.178584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:47:43.957370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:47:44.343268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:47:44.789549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:47:45.295236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:47:44.058654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:47:44.483300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:47:44.909286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:47:45.413561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:47:44.153394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:47:44.598331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:47:45.017498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:47:47.953444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세연도세목명과세건수과세금액비과세건수비과세금액
과세연도1.0000.0000.0000.0000.0000.000
세목명0.0001.0001.0000.8000.8130.763
과세건수0.0001.0001.0000.7790.6900.179
과세금액0.0000.8000.7791.0000.7120.853
비과세건수0.0000.8130.6900.7121.0000.824
비과세금액0.0000.7630.1790.8530.8241.000
2023-12-13T02:47:48.044586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세연도세목명
과세연도1.0000.000
세목명0.0001.000
2023-12-13T02:47:48.132328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세건수과세금액비과세건수비과세금액과세연도세목명
과세건수1.0000.6480.7040.4960.0000.930
과세금액0.6481.0000.5770.5470.0000.487
비과세건수0.7040.5771.0000.9360.0000.504
비과세금액0.4960.5470.9361.0000.0000.477
과세연도0.0000.0000.0000.0001.0000.000
세목명0.9300.4870.5040.4770.0001.000

Missing values

2023-12-13T02:47:45.564543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:47:45.716406image/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

시도명시군구명자치단체코드과세연도세목명과세건수과세금액비과세건수비과세금액
0대전광역시중구301402017취득세95583830949000045095376111000
1대전광역시중구301402017등록세005809000
2대전광역시중구301402017주민세1098686153518000113351628451000
3대전광역시중구301402017재산세121554291880500002045318604011000
4대전광역시중구301402017자동차세15893718397646000194251239573000
5대전광역시중구301402017레저세0000
6대전광역시중구301402017담배소비세0000
7대전광역시중구301402017지방소비세0000
8대전광역시중구301402017등록면허세6528246362940001376139306000
9대전광역시중구301402017도시계획세0000
시도명시군구명자치단체코드과세연도세목명과세건수과세금액비과세건수비과세금액
54대전광역시중구301402021재산세125954332064320002906123327301000
55대전광역시중구301402021자동차세15638418774985000253361176856000
56대전광역시중구301402021레저세0000
57대전광역시중구301402021담배소비세0000
58대전광역시중구301402021지방소비세7718860800000
59대전광역시중구301402021등록면허세706975374743000257454325000
60대전광역시중구301402021도시계획세0000
61대전광역시중구301402021지역자원시설세12397141880840001346526386000
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