Overview

Dataset statistics

Number of variables9
Number of observations46
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 KiB
Average record size in memory80.9 B

Variable types

Categorical5
Numeric4

Dataset

Description부산광역시해운대구_지방세과세현황_20201231
Author부산광역시 해운대구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15078914

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 1 other fieldsHigh correlation
비과세건수 is highly overall correlated with 과세건수 and 1 other fieldsHigh correlation
비과세금액 is highly overall correlated with 비과세건수High correlation
세목명 is highly overall correlated with 과세건수 and 1 other fieldsHigh correlation
과세건수 has 13 (28.3%) zerosZeros
과세금액 has 14 (30.4%) zerosZeros
비과세건수 has 17 (37.0%) zerosZeros
비과세금액 has 19 (41.3%) zerosZeros

Reproduction

Analysis started2023-12-10 16:59:41.317906
Analysis finished2023-12-10 16:59:45.011379
Duration3.69 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size500.0 B
부산광역시
46 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부산광역시 46
100.0%

Length

2023-12-11T01:59:45.215300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:59:45.378003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 46
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size500.0 B
해운대구
46 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
해운대구 46
100.0%

Length

2023-12-11T01:59:45.541687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:59:45.691045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
해운대구 46
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size500.0 B
26350
46 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row26350
2nd row26350
3rd row26350
4th row26350
5th row26350

Common Values

ValueCountFrequency (%)
26350 46
100.0%

Length

2023-12-11T01:59:45.869591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:59:46.030560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
26350 46
100.0%

과세년도
Categorical

Distinct4
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size500.0 B
2020
13 
2017
12 
2019
12 
2018
9 

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 (%)
2020 13
28.3%
2017 12
26.1%
2019 12
26.1%
2018 9
19.6%

Length

2023-12-11T01:59:46.182216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:59:46.367768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 13
28.3%
2017 12
26.1%
2019 12
26.1%
2018 9
19.6%

세목명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)28.3%
Missing0
Missing (%)0.0%
Memory size500.0 B
취득세
4 
주민세
4 
재산세
4 
자동차세
4 
등록면허세
4 
Other values (8)
26 

Length

Max length7
Median length5
Mean length4.173913
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
취득세 4
8.7%
주민세 4
8.7%
재산세 4
8.7%
자동차세 4
8.7%
등록면허세 4
8.7%
지역자원시설세 4
8.7%
지방소득세 4
8.7%
교육세 4
8.7%
레저세 3
 
6.5%
담배소비세 3
 
6.5%
Other values (3) 8
17.4%

Length

2023-12-11T01:59:46.641488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
취득세 4
8.7%
주민세 4
8.7%
재산세 4
8.7%
자동차세 4
8.7%
등록면허세 4
8.7%
지역자원시설세 4
8.7%
지방소득세 4
8.7%
교육세 4
8.7%
레저세 3
 
6.5%
담배소비세 3
 
6.5%
Other values (3) 8
17.4%

과세건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)73.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean186499.93
Minimum0
Maximum919855
Zeros13
Zeros (%)28.3%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-11T01:59:46.942304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median129429
Q3251121.25
95-th percentile858337.75
Maximum919855
Range919855
Interquartile range (IQR)251121.25

Descriptive statistics

Standard deviation241569.67
Coefficient of variation (CV)1.2952802
Kurtosis3.7274591
Mean186499.93
Median Absolute Deviation (MAD)126296
Skewness2.0015396
Sum8578997
Variance5.8355908 × 1010
MonotonicityNot monotonic
2023-12-11T01:59:47.185282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 13
28.3%
21414 1
 
2.2%
175051 1
 
2.2%
247936 1
 
2.2%
120864 1
 
2.2%
323196 1
 
2.2%
132299 1
 
2.2%
866095 1
 
2.2%
30687 1
 
2.2%
260861 1
 
2.2%
Other values (24) 24
52.2%
ValueCountFrequency (%)
0 13
28.3%
4 1
 
2.2%
18014 1
 
2.2%
18549 1
 
2.2%
21414 1
 
2.2%
30687 1
 
2.2%
113983 1
 
2.2%
118252 1
 
2.2%
119382 1
 
2.2%
120864 1
 
2.2%
ValueCountFrequency (%)
919855 1
2.2%
866095 1
2.2%
858693 1
2.2%
857272 1
2.2%
333187 1
2.2%
323196 1
2.2%
308519 1
2.2%
300400 1
2.2%
260861 1
2.2%
252596 1
2.2%

과세금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)71.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7657454 × 1010
Minimum0
Maximum4.1303 × 1011
Zeros14
Zeros (%)30.4%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-11T01:59:47.413867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.7380692 × 1010
Q38.0882846 × 1010
95-th percentile1.3820695 × 1011
Maximum4.1303 × 1011
Range4.1303 × 1011
Interquartile range (IQR)8.0882846 × 1010

Descriptive statistics

Standard deviation7.2438426 × 1010
Coefficient of variation (CV)1.519981
Kurtosis13.630249
Mean4.7657454 × 1010
Median Absolute Deviation (MAD)1.7380692 × 1010
Skewness3.127429
Sum2.1922429 × 1012
Variance5.2473256 × 1021
MonotonicityNot monotonic
2023-12-11T01:59:47.662039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 14
30.4%
144651984000 1
 
2.2%
10382138000 1
 
2.2%
33622958000 1
 
2.2%
13780335000 1
 
2.2%
15821521000 1
 
2.2%
121083380000 1
 
2.2%
37017918000 1
 
2.2%
413030000000 1
 
2.2%
117074000000 1
 
2.2%
Other values (23) 23
50.0%
ValueCountFrequency (%)
0 14
30.4%
8975520000 1
 
2.2%
9573594000 1
 
2.2%
10382138000 1
 
2.2%
10463821000 1
 
2.2%
13223527000 1
 
2.2%
13780335000 1
 
2.2%
15641788000 1
 
2.2%
15821521000 1
 
2.2%
16823571000 1
 
2.2%
ValueCountFrequency (%)
413030000000 1
2.2%
144651984000 1
2.2%
141427000000 1
2.2%
128546786000 1
2.2%
126878338000 1
2.2%
121083380000 1
2.2%
117074000000 1
2.2%
115536222000 1
2.2%
108838525000 1
2.2%
102611502000 1
2.2%

비과세건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)63.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11189.978
Minimum0
Maximum60254
Zeros17
Zeros (%)37.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-11T01:59:47.916117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1980
Q314473.75
95-th percentile47721.25
Maximum60254
Range60254
Interquartile range (IQR)14473.75

Descriptive statistics

Standard deviation17001.288
Coefficient of variation (CV)1.5193316
Kurtosis1.5434058
Mean11189.978
Median Absolute Deviation (MAD)1980
Skewness1.6347913
Sum514739
Variance2.8904379 × 108
MonotonicityNot monotonic
2023-12-11T01:59:48.125304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 17
37.0%
9 2
 
4.3%
13540 1
 
2.2%
21755 1
 
2.2%
2079 1
 
2.2%
6631 1
 
2.2%
47764 1
 
2.2%
60254 1
 
2.2%
14455 1
 
2.2%
1 1
 
2.2%
Other values (19) 19
41.3%
ValueCountFrequency (%)
0 17
37.0%
1 1
 
2.2%
2 1
 
2.2%
7 1
 
2.2%
9 2
 
4.3%
1881 1
 
2.2%
2079 1
 
2.2%
3037 1
 
2.2%
4509 1
 
2.2%
5302 1
 
2.2%
ValueCountFrequency (%)
60254 1
2.2%
53708 1
2.2%
47764 1
2.2%
47593 1
2.2%
44922 1
2.2%
41776 1
2.2%
37487 1
2.2%
21755 1
2.2%
18962 1
2.2%
17250 1
2.2%

비과세금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)60.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1299338 × 109
Minimum0
Maximum4.8253426 × 1010
Zeros19
Zeros (%)41.3%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-11T01:59:48.420328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.99504 × 108
Q32.0356598 × 109
95-th percentile3.9118917 × 1010
Maximum4.8253426 × 1010
Range4.8253426 × 1010
Interquartile range (IQR)2.0356598 × 109

Descriptive statistics

Standard deviation1.3064555 × 1010
Coefficient of variation (CV)2.1312717
Kurtosis3.7148224
Mean6.1299338 × 109
Median Absolute Deviation (MAD)2.99504 × 108
Skewness2.216509
Sum2.8197696 × 1011
Variance1.7068259 × 1020
MonotonicityNot monotonic
2023-12-11T01:59:48.691567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 19
41.3%
22254713000 1
 
2.2%
1415929000 1
 
2.2%
977891000 1
 
2.2%
301035000 1
 
2.2%
1926891000 1
 
2.2%
48253426000 1
 
2.2%
1006573000 1
 
2.2%
3091830000 1
 
2.2%
16919248000 1
 
2.2%
Other values (18) 18
39.1%
ValueCountFrequency (%)
0 19
41.3%
1000 1
 
2.2%
14273000 1
 
2.2%
273017000 1
 
2.2%
297973000 1
 
2.2%
301035000 1
 
2.2%
303944000 1
 
2.2%
961889000 1
 
2.2%
977891000 1
 
2.2%
1006573000 1
 
2.2%
ValueCountFrequency (%)
48253426000 1
2.2%
44687492000 1
2.2%
40270556000 1
2.2%
35664000000 1
2.2%
28582176000 1
2.2%
23533099000 1
2.2%
22254713000 1
2.2%
16919248000 1
2.2%
3091830000 1
2.2%
2112273000 1
2.2%

Interactions

2023-12-11T01:59:43.929860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:59:41.734601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:59:42.428855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:59:43.343616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:59:44.067710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:59:41.933702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:59:42.576795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:59:43.506737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:59:44.220165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:59:42.091864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:59:42.715213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:59:43.639409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:59:44.390386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:59:42.260362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:59:43.204816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:59:43.781257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:59:48.897524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명과세건수과세금액비과세건수비과세금액
과세년도1.0000.0000.0000.0000.0000.000
세목명0.0001.0001.0000.7770.7410.576
과세건수0.0001.0001.0000.5900.6140.213
과세금액0.0000.7770.5901.0000.4250.817
비과세건수0.0000.7410.6140.4251.0000.788
비과세금액0.0000.5760.2130.8170.7881.000
2023-12-11T01:59:49.063339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도
세목명1.0000.000
과세년도0.0001.000
2023-12-11T01:59:49.221395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세건수과세금액비과세건수비과세금액과세년도세목명
과세건수1.0000.5560.5560.3770.0000.897
과세금액0.5561.0000.4790.4690.0000.505
비과세건수0.5560.4791.0000.9170.0000.417
비과세금액0.3770.4690.9171.0000.0000.277
과세년도0.0000.0000.0000.0001.0000.000
세목명0.8970.5050.4170.2770.0001.000

Missing values

2023-12-11T01:59:44.598847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:59:44.885408image/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
0부산광역시해운대구263502017취득세214141446519840001354022254713000
1부산광역시해운대구263502017주민세1729308975520000111931429547000
2부산광역시해운대구263502017재산세237910856428500001896235664000000
3부산광역시해운대구263502017자동차세25259633252280000374872071916000
4부산광역시해운대구263502017레저세0000
5부산광역시해운대구263502017담배소비세0000
6부산광역시해운대구263502017지방소비세0000
7부산광역시해운대구263502017등록면허세119382132235270003037303944000
8부산광역시해운대구263502017도시계획세0000
9부산광역시해운대구263502017지역자원시설세3004001564178800078331026445000
시도명시군구명자치단체코드과세년도세목명과세건수과세금액비과세건수비과세금액
36부산광역시해운대구263502020재산세2608611170740000006025448253426000
37부산광역시해운대구263502020자동차세25122133951687000477641926891000
38부산광역시해운대구263502020레저세0000
39부산광역시해운대구263502020담배소비세0000
40부산광역시해운대구263502020지방소비세4000
41부산광역시해운대구263502020등록면허세150432237999080006631301035000
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