Overview

Dataset statistics

Number of variables9
Number of observations26
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 KiB
Average record size in memory83.1 B

Variable types

Categorical4
Text1
Numeric4

Dataset

Description부산광역시사상구_지방세과세현황_20211231
Author부산광역시 사상구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15079670

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
과세건수 is highly overall correlated with 과세금액 and 1 other fieldsHigh correlation
과세금액 is highly overall correlated with 과세건수 and 2 other fieldsHigh correlation
비과세건수 is highly overall correlated with 과세건수 and 2 other fieldsHigh correlation
비과세금액 is highly overall correlated with 과세금액 and 1 other fieldsHigh correlation
과세건수 has 8 (30.8%) zerosZeros
과세금액 has 8 (30.8%) zerosZeros
비과세건수 has 10 (38.5%) zerosZeros
비과세금액 has 12 (46.2%) zerosZeros

Reproduction

Analysis started2023-12-10 16:55:11.448189
Analysis finished2023-12-10 16:55:15.052163
Duration3.6 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size340.0 B
부산광역시
26 

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 26
100.0%

Length

2023-12-11T01:55:15.171642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:55:15.348594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 26
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size340.0 B
사상구
26 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
사상구 26
100.0%

Length

2023-12-11T01:55:15.502427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:55:15.680435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사상구 26
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size340.0 B
26530
26 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row26530
2nd row26530
3rd row26530
4th row26530
5th row26530

Common Values

ValueCountFrequency (%)
26530 26
100.0%

Length

2023-12-11T01:55:15.839629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:55:15.996021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
26530 26
100.0%

과세년도
Categorical

Distinct2
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size340.0 B
2020
13 
2021
13 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 13
50.0%
2021 13
50.0%

Length

2023-12-11T01:55:16.163033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:55:16.337558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 13
50.0%
2021 13
50.0%

세목명
Text

Distinct13
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size340.0 B
2023-12-11T01:55:16.574754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length4.1538462
Min length3

Characters and Unicode

Total characters108
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row취득세
2nd row등록세
3rd row주민세
4th row재산세
5th row자동차세
ValueCountFrequency (%)
취득세 2
 
7.7%
등록세 2
 
7.7%
주민세 2
 
7.7%
재산세 2
 
7.7%
자동차세 2
 
7.7%
레저세 2
 
7.7%
담배소비세 2
 
7.7%
지방소비세 2
 
7.7%
등록면허세 2
 
7.7%
도시계획세 2
 
7.7%
Other values (3) 6
23.1%
2023-12-11T01:55:17.068264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
세 26
24.1%
지 6
 
5.6%
소 6
 
5.6%
방 4
 
3.7%
시 4
 
3.7%
등 4
 
3.7%
록 4
 
3.7%
비 4
 
3.7%
득 4
 
3.7%
자 4
 
3.7%
Other values (21) 42
38.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 108
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
세 26
24.1%
지 6
 
5.6%
소 6
 
5.6%
방 4
 
3.7%
시 4
 
3.7%
등 4
 
3.7%
록 4
 
3.7%
비 4
 
3.7%
득 4
 
3.7%
자 4
 
3.7%
Other values (21) 42
38.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 108
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
세 26
24.1%
지 6
 
5.6%
소 6
 
5.6%
방 4
 
3.7%
시 4
 
3.7%
등 4
 
3.7%
록 4
 
3.7%
비 4
 
3.7%
득 4
 
3.7%
자 4
 
3.7%
Other values (21) 42
38.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 108
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
세 26
24.1%
지 6
 
5.6%
소 6
 
5.6%
방 4
 
3.7%
시 4
 
3.7%
등 4
 
3.7%
록 4
 
3.7%
비 4
 
3.7%
득 4
 
3.7%
자 4
 
3.7%
Other values (21) 42
38.9%

과세건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)73.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86563.308
Minimum0
Maximum439256
Zeros8
Zeros (%)30.8%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T01:55:17.270339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median74742.5
Q3124590.75
95-th percentile364045
Maximum439256
Range439256
Interquartile range (IQR)124590.75

Descriptive statistics

Standard deviation118132.43
Coefficient of variation (CV)1.3646941
Kurtosis4.7124597
Mean86563.308
Median Absolute Deviation (MAD)72362.5
Skewness2.1000197
Sum2250646
Variance1.3955272 × 1010
MonotonicityNot monotonic
2023-12-11T01:55:17.552936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 8
30.8%
9039 1
 
3.8%
435941 1
 
3.8%
95494 1
 
3.8%
148357 1
 
3.8%
73963 1
 
3.8%
7 1
 
3.8%
132459 1
 
3.8%
125070 1
 
3.8%
104274 1
 
3.8%
Other values (9) 9
34.6%
ValueCountFrequency (%)
0 8
30.8%
6 1
 
3.8%
7 1
 
3.8%
9039 1
 
3.8%
10378 1
 
3.8%
73963 1
 
3.8%
75522 1
 
3.8%
89499 1
 
3.8%
95494 1
 
3.8%
104274 1
 
3.8%
ValueCountFrequency (%)
439256 1
3.8%
435941 1
3.8%
148357 1
3.8%
145853 1
3.8%
134202 1
3.8%
132459 1
3.8%
125070 1
3.8%
123153 1
3.8%
108173 1
3.8%
104274 1
3.8%

과세금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)73.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5439531 × 1010
Minimum0
Maximum5.6231227 × 1010
Zeros8
Zeros (%)30.8%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T01:55:17.795565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6.3451885 × 109
Q31.6797712 × 1010
95-th percentile5.0850777 × 1010
Maximum5.6231227 × 1010
Range5.6231227 × 1010
Interquartile range (IQR)1.6797712 × 1010

Descriptive statistics

Standard deviation1.9285032 × 1010
Coefficient of variation (CV)1.2490685
Kurtosis-0.29690711
Mean1.5439531 × 1010
Median Absolute Deviation (MAD)6.3451885 × 109
Skewness1.1477872
Sum4.014278 × 1011
Variance3.7191245 × 1020
MonotonicityNot monotonic
2023-12-11T01:55:17.992598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 8
30.8%
45196825000 1
 
3.8%
17014458000 1
 
3.8%
44797244000 1
 
3.8%
5008133000 1
 
3.8%
6092820000 1
 
3.8%
4443121000 1
 
3.8%
15539346000 1
 
3.8%
51353230000 1
 
3.8%
7834432000 1
 
3.8%
Other values (9) 9
34.6%
ValueCountFrequency (%)
0 8
30.8%
4277000000 1
 
3.8%
4443121000 1
 
3.8%
4952494000 1
 
3.8%
5008133000 1
 
3.8%
6092820000 1
 
3.8%
6597557000 1
 
3.8%
7497706000 1
 
3.8%
7834432000 1
 
3.8%
15539346000 1
 
3.8%
ValueCountFrequency (%)
56231227000 1
3.8%
51353230000 1
3.8%
49343418000 1
3.8%
45196825000 1
3.8%
44797244000 1
3.8%
43482109000 1
3.8%
17014458000 1
3.8%
16147473000 1
3.8%
15619206000 1
3.8%
15539346000 1
3.8%

비과세건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)65.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8224.6538
Minimum0
Maximum46395
Zeros10
Zeros (%)38.5%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T01:55:18.171560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q36532.75
95-th percentile41048.75
Maximum46395
Range46395
Interquartile range (IQR)6532.75

Descriptive statistics

Standard deviation14292.815
Coefficient of variation (CV)1.7378014
Kurtosis2.1390366
Mean8224.6538
Median Absolute Deviation (MAD)5
Skewness1.7743506
Sum213841
Variance2.0428457 × 108
MonotonicityNot monotonic
2023-12-11T01:55:18.378542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 10
38.5%
22973 1
 
3.8%
3 1
 
3.8%
6 1
 
3.8%
1281 1
 
3.8%
3500 1
 
3.8%
46395 1
 
3.8%
26351 1
 
3.8%
6154 1
 
3.8%
24681 1
 
3.8%
Other values (7) 7
26.9%
ValueCountFrequency (%)
0 10
38.5%
1 1
 
3.8%
3 1
 
3.8%
4 1
 
3.8%
6 1
 
3.8%
932 1
 
3.8%
1281 1
 
3.8%
3190 1
 
3.8%
3500 1
 
3.8%
6154 1
 
3.8%
ValueCountFrequency (%)
46395 1
3.8%
45948 1
3.8%
26351 1
3.8%
25763 1
3.8%
24681 1
3.8%
22973 1
3.8%
6659 1
3.8%
6154 1
3.8%
3500 1
3.8%
3190 1
3.8%

비과세금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)57.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.686895 × 109
Minimum0
Maximum2.4525334 × 1010
Zeros12
Zeros (%)46.2%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T01:55:18.559976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1970000
Q31.4545952 × 109
95-th percentile2.166387 × 1010
Maximum2.4525334 × 1010
Range2.4525334 × 1010
Interquartile range (IQR)1.4545952 × 109

Descriptive statistics

Standard deviation8.0423605 × 109
Coefficient of variation (CV)2.181337
Kurtosis2.440388
Mean3.686895 × 109
Median Absolute Deviation (MAD)1970000
Skewness2.0410201
Sum9.585927 × 1010
Variance6.4679562 × 1019
MonotonicityNot monotonic
2023-12-11T01:55:18.780689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 12
46.2%
21605345000 1
 
3.8%
3417000 1
 
3.8%
1457464000 1
 
3.8%
20519179000 1
 
3.8%
1781868000 1
 
3.8%
88516000 1
 
3.8%
448748000 1
 
3.8%
24525334000 1
 
3.8%
523000 1
 
3.8%
Other values (5) 5
19.2%
ValueCountFrequency (%)
0 12
46.2%
523000 1
 
3.8%
3417000 1
 
3.8%
74973000 1
 
3.8%
88516000 1
 
3.8%
448748000 1
 
3.8%
462544000 1
 
3.8%
1445989000 1
 
3.8%
1457464000 1
 
3.8%
1761991000 1
 
3.8%
ValueCountFrequency (%)
24525334000 1
3.8%
21683379000 1
3.8%
21605345000 1
3.8%
20519179000 1
3.8%
1781868000 1
3.8%
1761991000 1
3.8%
1457464000 1
3.8%
1445989000 1
3.8%
462544000 1
3.8%
448748000 1
3.8%

Interactions

2023-12-11T01:55:13.372715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:55:11.830204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:55:12.327773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:55:12.876669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:55:13.503561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:55:11.947810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:55:12.453834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:55:12.990992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:55:13.694168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:55:12.075658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:55:12.591082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:55:13.129157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:55:14.331340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:55:12.191642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:55:12.726137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:55:13.248769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:55:18.985321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명과세건수과세금액비과세건수비과세금액
과세년도1.0000.0000.0000.0000.0000.000
세목명0.0001.0001.0000.8940.9290.771
과세건수0.0001.0001.0000.7410.6760.000
과세금액0.0000.8940.7411.0000.8090.801
비과세건수0.0000.9290.6760.8091.0000.705
비과세금액0.0000.7710.0000.8010.7051.000
2023-12-11T01:55:19.154731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세건수과세금액비과세건수비과세금액과세년도
과세건수1.0000.6950.6200.4220.000
과세금액0.6951.0000.6520.5840.000
비과세건수0.6200.6521.0000.9270.000
비과세금액0.4220.5840.9271.0000.000
과세년도0.0000.0000.0000.0001.000

Missing values

2023-12-11T01:55:14.589317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:55:14.948874image/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
0부산광역시사상구265302020취득세9039451968250002297321605345000
1부산광역시사상구265302020등록세0013417000
2부산광역시사상구265302020주민세108173749770600066591457464000
3부산광역시사상구265302020재산세123153493434180002576320519179000
4부산광역시사상구265302020자동차세13420215619206000459481781868000
5부산광역시사상구265302020레저세0000
6부산광역시사상구265302020담배소비세0000
7부산광역시사상구265302020지방소비세6427700000000
8부산광역시사상구265302020등록면허세755226597557000319088516000
9부산광역시사상구265302020도시계획세0000
시도명시군구명자치단체코드과세년도세목명과세건수과세금액비과세건수비과세금액
16부산광역시사상구265302021재산세125070513532300002635121683379000
17부산광역시사상구265302021자동차세13245915539346000463951761991000
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