Overview

Dataset statistics

Number of variables9
Number of observations76
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.9 KiB
Average record size in memory79.7 B

Variable types

Categorical4
Numeric5

Dataset

Description부산광역시 강서구 지방세 과세 현황에 대한 데이터로, 연도별 지방세 과세 및 비과세 현황을 세목별로 제공하고 있습니다. 이 데이터 자료는 국민 조세 혜택 규모를 파악하는 데 사용합니다.
URLhttps://www.data.go.kr/data/15078285/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 3 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 과세건수 and 1 other fieldsHigh correlation
세목명 is highly overall correlated with 과세건수 and 1 other fieldsHigh correlation
과세건수 has 18 (23.7%) zerosZeros
과세금액 has 19 (25.0%) zerosZeros
비과세건수 has 31 (40.8%) zerosZeros
비과세금액 has 31 (40.8%) zerosZeros

Reproduction

Analysis started2023-12-12 23:46:17.089179
Analysis finished2023-12-12 23:46:19.641271
Duration2.55 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size740.0 B
부산광역시
76 

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 76
100.0%

Length

2023-12-13T08:46:19.701128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:46:19.813275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 76
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size740.0 B
강서구
76 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
강서구 76
100.0%

Length

2023-12-13T08:46:19.915774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:46:20.001302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강서구 76
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size740.0 B
26440
76 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row26440
2nd row26440
3rd row26440
4th row26440
5th row26440

Common Values

ValueCountFrequency (%)
26440 76
100.0%

Length

2023-12-13T08:46:20.093828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:46:20.177099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
26440 76
100.0%

과세년도
Real number (ℝ)

Distinct6
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.4474
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size816.0 B
2023-12-13T08:46:20.264467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018
median2019
Q32021
95-th percentile2022
Maximum2022
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7079792
Coefficient of variation (CV)0.00084576565
Kurtosis-1.249367
Mean2019.4474
Median Absolute Deviation (MAD)1
Skewness0.042755576
Sum153478
Variance2.917193
MonotonicityIncreasing
2023-12-13T08:46:20.364087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2017 13
17.1%
2018 13
17.1%
2019 13
17.1%
2020 13
17.1%
2021 12
15.8%
2022 12
15.8%
ValueCountFrequency (%)
2017 13
17.1%
2018 13
17.1%
2019 13
17.1%
2020 13
17.1%
2021 12
15.8%
2022 12
15.8%
ValueCountFrequency (%)
2022 12
15.8%
2021 12
15.8%
2020 13
17.1%
2019 13
17.1%
2018 13
17.1%
2017 13
17.1%

세목명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)17.1%
Missing0
Missing (%)0.0%
Memory size740.0 B
취득세
6 
주민세
6 
재산세
6 
자동차세
6 
레저세
6 
Other values (8)
46 

Length

Max length7
Median length5
Mean length4.1842105
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
취득세 6
 
7.9%
주민세 6
 
7.9%
재산세 6
 
7.9%
자동차세 6
 
7.9%
레저세 6
 
7.9%
담배소비세 6
 
7.9%
지방소비세 6
 
7.9%
등록면허세 6
 
7.9%
도시계획세 6
 
7.9%
지역자원시설세 6
 
7.9%
Other values (3) 16
21.1%

Length

2023-12-13T08:46:20.497986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
취득세 6
 
7.9%
주민세 6
 
7.9%
재산세 6
 
7.9%
자동차세 6
 
7.9%
레저세 6
 
7.9%
담배소비세 6
 
7.9%
지방소비세 6
 
7.9%
등록면허세 6
 
7.9%
도시계획세 6
 
7.9%
지역자원시설세 6
 
7.9%
Other values (3) 16
21.1%

과세건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct57
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73609.934
Minimum0
Maximum384412
Zeros18
Zeros (%)23.7%
Negative0
Negative (%)0.0%
Memory size816.0 B
2023-12-13T08:46:20.612767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.5
median71528
Q3102828.75
95-th percentile326800
Maximum384412
Range384412
Interquartile range (IQR)102825.25

Descriptive statistics

Standard deviation94761.183
Coefficient of variation (CV)1.2873423
Kurtosis3.8786058
Mean73609.934
Median Absolute Deviation (MAD)58441.5
Skewness1.962848
Sum5594355
Variance8.9796818 × 109
MonotonicityNot monotonic
2023-12-13T08:46:20.742028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 18
 
23.7%
4 2
 
2.6%
24 2
 
2.6%
114991 1
 
1.3%
121058 1
 
1.3%
14 1
 
1.3%
82565 1
 
1.3%
2 1
 
1.3%
100097 1
 
1.3%
98060 1
 
1.3%
Other values (47) 47
61.8%
ValueCountFrequency (%)
0 18
23.7%
2 1
 
1.3%
4 2
 
2.6%
9 1
 
1.3%
14 1
 
1.3%
24 2
 
2.6%
26 1
 
1.3%
28 1
 
1.3%
69 1
 
1.3%
9411 1
 
1.3%
ValueCountFrequency (%)
384412 1
1.3%
376301 1
1.3%
371403 1
1.3%
346576 1
1.3%
320208 1
1.3%
313053 1
1.3%
132114 1
1.3%
128347 1
1.3%
125961 1
1.3%
125123 1
1.3%

과세금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct58
Distinct (%)76.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6058876 × 1010
Minimum0
Maximum1.50199 × 1011
Zeros19
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size816.0 B
2023-12-13T08:46:20.878397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1245250
median1.4166656 × 1010
Q37.0187358 × 1010
95-th percentile1.1730425 × 1011
Maximum1.50199 × 1011
Range1.50199 × 1011
Interquartile range (IQR)7.0187113 × 1010

Descriptive statistics

Standard deviation4.2609273 × 1010
Coefficient of variation (CV)1.1816584
Kurtosis-0.20654108
Mean3.6058876 × 1010
Median Absolute Deviation (MAD)1.4166656 × 1010
Skewness1.0485147
Sum2.7404746 × 1012
Variance1.8155501 × 1021
MonotonicityNot monotonic
2023-12-13T08:46:21.362401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19
 
25.0%
98384377000 1
 
1.3%
15159469000 1
 
1.3%
9419686000 1
 
1.3%
914500000 1
 
1.3%
13096968000 1
 
1.3%
327000 1
 
1.3%
12292782000 1
 
1.3%
89241630000 1
 
1.3%
34774705000 1
 
1.3%
Other values (48) 48
63.2%
ValueCountFrequency (%)
0 19
25.0%
327000 1
 
1.3%
914500000 1
 
1.3%
1028250000 1
 
1.3%
4944578000 1
 
1.3%
6224367000 1
 
1.3%
8965715000 1
 
1.3%
9419686000 1
 
1.3%
10430350000 1
 
1.3%
10717521000 1
 
1.3%
ValueCountFrequency (%)
150199000000 1
1.3%
138303000000 1
1.3%
126348000000 1
1.3%
119921000000 1
1.3%
116432000000 1
1.3%
112651000000 1
1.3%
111052000000 1
1.3%
109282000000 1
1.3%
108973000000 1
1.3%
98384377000 1
1.3%

비과세건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct45
Distinct (%)59.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4347.5263
Minimum0
Maximum34784
Zeros31
Zeros (%)40.8%
Negative0
Negative (%)0.0%
Memory size816.0 B
2023-12-13T08:46:21.499487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median16
Q35798.5
95-th percentile23290.25
Maximum34784
Range34784
Interquartile range (IQR)5798.5

Descriptive statistics

Standard deviation8030.9819
Coefficient of variation (CV)1.8472532
Kurtosis5.2612592
Mean4347.5263
Median Absolute Deviation (MAD)16
Skewness2.3784003
Sum330412
Variance64496670
MonotonicityNot monotonic
2023-12-13T08:46:21.613689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0 31
40.8%
2 2
 
2.6%
16856 1
 
1.3%
6001 1
 
1.3%
32663 1
 
1.3%
15118 1
 
1.3%
2098 1
 
1.3%
723 1
 
1.3%
9 1
 
1.3%
5887 1
 
1.3%
Other values (35) 35
46.1%
ValueCountFrequency (%)
0 31
40.8%
2 2
 
2.6%
4 1
 
1.3%
5 1
 
1.3%
6 1
 
1.3%
9 1
 
1.3%
10 1
 
1.3%
22 1
 
1.3%
34 1
 
1.3%
723 1
 
1.3%
ValueCountFrequency (%)
34784 1
1.3%
32663 1
1.3%
30100 1
1.3%
26846 1
1.3%
22105 1
1.3%
18046 1
1.3%
16856 1
1.3%
16787 1
1.3%
15118 1
1.3%
12369 1
1.3%

비과세금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct45
Distinct (%)59.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4396295 × 109
Minimum0
Maximum5.9503026 × 1010
Zeros31
Zeros (%)40.8%
Negative0
Negative (%)0.0%
Memory size816.0 B
2023-12-13T08:46:21.733003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1629000
Q35.3610025 × 108
95-th percentile5.0693912 × 1010
Maximum5.9503026 × 1010
Range5.9503026 × 1010
Interquartile range (IQR)5.3610025 × 108

Descriptive statistics

Standard deviation1.7363596 × 1010
Coefficient of variation (CV)2.3339329
Kurtosis2.5768888
Mean7.4396295 × 109
Median Absolute Deviation (MAD)1629000
Skewness2.0689121
Sum5.6541184 × 1011
Variance3.0149447 × 1020
MonotonicityNot monotonic
2023-12-13T08:46:21.854277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0 31
40.8%
2000 2
 
2.6%
45666192000 1
 
1.3%
612997000 1
 
1.3%
93728000 1
 
1.3%
53155175000 1
 
1.3%
590956000 1
 
1.3%
132311000 1
 
1.3%
473407000 1
 
1.3%
29913964000 1
 
1.3%
Other values (35) 35
46.1%
ValueCountFrequency (%)
0 31
40.8%
1000 1
 
1.3%
2000 2
 
2.6%
4000 1
 
1.3%
8000 1
 
1.3%
276000 1
 
1.3%
384000 1
 
1.3%
2874000 1
 
1.3%
3561000 1
 
1.3%
25994000 1
 
1.3%
ValueCountFrequency (%)
59503026000 1
1.3%
56532822000 1
1.3%
53155175000 1
1.3%
52700596000 1
1.3%
50025017000 1
1.3%
47257849000 1
1.3%
46174597000 1
1.3%
45666192000 1
1.3%
45422458000 1
1.3%
40154314000 1
1.3%

Interactions

2023-12-13T08:46:18.990489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:17.325785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:17.712489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:18.139898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:18.556106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:19.075718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:17.404161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:17.790009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:18.214275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:18.629219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:19.177238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:17.483117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:17.870543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:18.296703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:18.717468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:19.259112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:17.564272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:17.960718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:18.381063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:18.810453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:19.341395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:17.642398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:18.058402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:18.475646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:18.904341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:46:21.936681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명과세건수과세금액비과세건수비과세금액
과세년도1.0000.0000.0000.0000.0000.000
세목명0.0001.0000.8530.8490.7030.652
과세건수0.0000.8531.0000.7550.4320.372
과세금액0.0000.8490.7551.0000.7930.818
비과세건수0.0000.7030.4320.7931.0000.773
비과세금액0.0000.6520.3720.8180.7731.000
2023-12-13T08:46:22.037701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도과세건수과세금액비과세건수비과세금액세목명
과세년도1.0000.1770.0860.0700.0260.000
과세건수0.1771.0000.6500.5730.5050.606
과세금액0.0860.6501.0000.4540.4650.555
비과세건수0.0700.5730.4541.0000.9500.373
비과세금액0.0260.5050.4650.9501.0000.372
세목명0.0000.6060.5550.3730.3721.000

Missing values

2023-12-13T08:46:19.465770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:46:19.594285image/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
0부산광역시강서구264402017취득세14379126348000000610745666192000
1부산광역시강서구264402017등록세002276000
2부산광역시강서구264402017주민세68627145801120001835536236000
3부산광역시강서구264402017재산세98364716578910001678746174597000
4부산광역시강서구264402017자동차세105324128190360008552543512000
5부산광역시강서구264402017레저세287172371000000
6부산광역시강서구264402017담배소비세0000
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