Overview

Dataset statistics

Number of variables9
Number of observations52
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.1 KiB
Average record size in memory80.5 B

Variable types

Categorical5
Numeric4

Dataset

Description경상남도 거창군 지방세 과세현황에 대한 데이터로 과세년도, 세목명, 과세건수, 과세금액 비과세건수, 비과세금액 항목을 제공합니다.
Author경상남도 거창군
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15079161

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 2 other fieldsHigh correlation
비과세금액 is highly overall correlated with 과세건수 and 2 other fieldsHigh correlation
세목명 is highly overall correlated with 과세건수 and 3 other fieldsHigh correlation
과세건수 has 15 (28.8%) zerosZeros
과세금액 has 15 (28.8%) zerosZeros
비과세건수 has 20 (38.5%) zerosZeros
비과세금액 has 20 (38.5%) zerosZeros

Reproduction

Analysis started2023-12-10 23:23:07.585838
Analysis finished2023-12-10 23:23:09.507326
Duration1.92 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size548.0 B
경상남도
52 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상남도 52
100.0%

Length

2023-12-11T08:23:09.562027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:23:09.644826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 52
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size548.0 B
거창군
52 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
거창군 52
100.0%

Length

2023-12-11T08:23:09.987205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:23:10.067495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
거창군 52
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size548.0 B
48880
52 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row48880
2nd row48880
3rd row48880
4th row48880
5th row48880

Common Values

ValueCountFrequency (%)
48880 52
100.0%

Length

2023-12-11T08:23:10.152619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:23:10.233290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48880 52
100.0%

과세년도
Categorical

Distinct4
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size548.0 B
2017
13 
2018
13 
2019
13 
2020
13 

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
25.0%
2018 13
25.0%
2019 13
25.0%
2020 13
25.0%

Length

2023-12-11T08:23:10.339568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:23:10.437187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 13
25.0%
2018 13
25.0%
2019 13
25.0%
2020 13
25.0%

세목명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size548.0 B
취득세
4 
등록세
4 
주민세
4 
재산세
4 
자동차세
4 
Other values (8)
32 

Length

Max length7
Median length5
Mean length4.1538462
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
취득세 4
 
7.7%
등록세 4
 
7.7%
주민세 4
 
7.7%
재산세 4
 
7.7%
자동차세 4
 
7.7%
레저세 4
 
7.7%
담배소비세 4
 
7.7%
지방소비세 4
 
7.7%
등록면허세 4
 
7.7%
도시계획세 4
 
7.7%
Other values (3) 12
23.1%

Length

2023-12-11T08:23:10.549183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
취득세 4
 
7.7%
등록세 4
 
7.7%
주민세 4
 
7.7%
재산세 4
 
7.7%
자동차세 4
 
7.7%
레저세 4
 
7.7%
담배소비세 4
 
7.7%
지방소비세 4
 
7.7%
등록면허세 4
 
7.7%
도시계획세 4
 
7.7%
Other values (3) 12
23.1%

과세건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)73.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29326.077
Minimum0
Maximum153635
Zeros15
Zeros (%)28.8%
Negative0
Negative (%)0.0%
Memory size600.0 B
2023-12-11T08:23:10.690199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median12747.5
Q330522.75
95-th percentile148305.85
Maximum153635
Range153635
Interquartile range (IQR)30522.75

Descriptive statistics

Standard deviation42578.318
Coefficient of variation (CV)1.4518927
Kurtosis2.9156693
Mean29326.077
Median Absolute Deviation (MAD)12747.5
Skewness1.921844
Sum1524956
Variance1.8129132 × 109
MonotonicityNot monotonic
2023-12-11T08:23:10.809377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 15
28.8%
29938 1
 
1.9%
88237 1
 
1.9%
44376 1
 
1.9%
81 1
 
1.9%
23355 1
 
1.9%
20472 1
 
1.9%
13194 1
 
1.9%
148885 1
 
1.9%
12045 1
 
1.9%
Other values (28) 28
53.8%
ValueCountFrequency (%)
0 15
28.8%
6 1
 
1.9%
81 1
 
1.9%
87 1
 
1.9%
109 1
 
1.9%
276 1
 
1.9%
11215 1
 
1.9%
11347 1
 
1.9%
12045 1
 
1.9%
12126 1
 
1.9%
ValueCountFrequency (%)
153635 1
1.9%
149320 1
1.9%
148885 1
1.9%
147832 1
1.9%
89595 1
1.9%
88237 1
1.9%
87379 1
1.9%
84318 1
1.9%
45902 1
1.9%
44376 1
1.9%

과세금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)73.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0593165 × 109
Minimum0
Maximum1.9181784 × 1010
Zeros15
Zeros (%)28.8%
Negative0
Negative (%)0.0%
Memory size600.0 B
2023-12-11T08:23:10.930185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.2599915 × 109
Q35.8859742 × 109
95-th percentile1.4543463 × 1010
Maximum1.9181784 × 1010
Range1.9181784 × 1010
Interquartile range (IQR)5.8859742 × 109

Descriptive statistics

Standard deviation4.8450705 × 109
Coefficient of variation (CV)1.1935681
Kurtosis1.9056902
Mean4.0593165 × 109
Median Absolute Deviation (MAD)1.2599915 × 109
Skewness1.4857011
Sum2.1108446 × 1011
Variance2.3474708 × 1019
MonotonicityNot monotonic
2023-12-11T08:23:11.071385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 15
28.8%
1139731000 1
 
1.9%
5908919000 1
 
1.9%
9527448000 1
 
1.9%
3961047000 1
 
1.9%
1185022000 1
 
1.9%
913142000 1
 
1.9%
6483608000 1
 
1.9%
4813112000 1
 
1.9%
17574816000 1
 
1.9%
Other values (28) 28
53.8%
ValueCountFrequency (%)
0 15
28.8%
765944000 1
 
1.9%
913142000 1
 
1.9%
943961000 1
 
1.9%
945587000 1
 
1.9%
985619000 1
 
1.9%
1091524000 1
 
1.9%
1116565000 1
 
1.9%
1139731000 1
 
1.9%
1185022000 1
 
1.9%
ValueCountFrequency (%)
19181784000 1
1.9%
17574816000 1
1.9%
16491397000 1
1.9%
12949698000 1
1.9%
10771066000 1
1.9%
10564068000 1
1.9%
10168298000 1
1.9%
9527448000 1
1.9%
7175553000 1
1.9%
6527288000 1
1.9%

비과세건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)63.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3706.1923
Minimum0
Maximum29110
Zeros20
Zeros (%)38.5%
Negative0
Negative (%)0.0%
Memory size600.0 B
2023-12-11T08:23:11.206162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median103
Q33609
95-th percentile23391.75
Maximum29110
Range29110
Interquartile range (IQR)3609

Descriptive statistics

Standard deviation6947.9367
Coefficient of variation (CV)1.8746833
Kurtosis6.3557949
Mean3706.1923
Median Absolute Deviation (MAD)103
Skewness2.6102191
Sum192722
Variance48273825
MonotonicityNot monotonic
2023-12-11T08:23:11.323657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 20
38.5%
2961 1
 
1.9%
5 1
 
1.9%
26736 1
 
1.9%
6741 1
 
1.9%
3338 1
 
1.9%
1580 1
 
1.9%
118 1
 
1.9%
2634 1
 
1.9%
6844 1
 
1.9%
Other values (23) 23
44.2%
ValueCountFrequency (%)
0 20
38.5%
5 1
 
1.9%
15 1
 
1.9%
25 1
 
1.9%
36 1
 
1.9%
95 1
 
1.9%
97 1
 
1.9%
109 1
 
1.9%
118 1
 
1.9%
1499 1
 
1.9%
ValueCountFrequency (%)
29110 1
1.9%
26736 1
1.9%
23807 1
1.9%
23052 1
1.9%
8551 1
1.9%
8054 1
1.9%
7664 1
1.9%
7518 1
1.9%
6844 1
1.9%
6741 1
1.9%

비과세금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)61.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7249477 × 108
Minimum0
Maximum3.970316 × 109
Zeros20
Zeros (%)38.5%
Negative0
Negative (%)0.0%
Memory size600.0 B
2023-12-11T08:23:11.449067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5480500
Q31.9354 × 108
95-th percentile3.5517294 × 109
Maximum3.970316 × 109
Range3.970316 × 109
Interquartile range (IQR)1.9354 × 108

Descriptive statistics

Standard deviation1.2200682 × 109
Coefficient of variation (CV)2.131143
Kurtosis2.3071587
Mean5.7249477 × 108
Median Absolute Deviation (MAD)5480500
Skewness2.0161554
Sum2.9769728 × 1010
Variance1.4885665 × 1018
MonotonicityNot monotonic
2023-12-11T08:23:11.577428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 20
38.5%
9000 2
 
3.8%
3025000 1
 
1.9%
40199000 1
 
1.9%
3769099000 1
 
1.9%
270202000 1
 
1.9%
103357000 1
 
1.9%
158065000 1
 
1.9%
2926192000 1
 
1.9%
22201000 1
 
1.9%
Other values (22) 22
42.3%
ValueCountFrequency (%)
0 20
38.5%
7000 1
 
1.9%
8000 1
 
1.9%
9000 2
 
3.8%
2208000 1
 
1.9%
3025000 1
 
1.9%
7936000 1
 
1.9%
10634000 1
 
1.9%
22201000 1
 
1.9%
40199000 1
 
1.9%
ValueCountFrequency (%)
3970316000 1
1.9%
3769099000 1
1.9%
3624468000 1
1.9%
3492216000 1
1.9%
3183973000 1
1.9%
3133637000 1
1.9%
2929484000 1
1.9%
2926192000 1
1.9%
285852000 1
1.9%
274541000 1
1.9%

Interactions

2023-12-11T08:23:08.925900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:23:07.836568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:23:08.206620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:23:08.569449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:23:09.017390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:23:07.922213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:23:08.312490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:23:08.660858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:23:09.124071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:23:08.014724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:23:08.399477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:23:08.756084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:23:09.200425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:23:08.109425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:23:08.480009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:23:08.840253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:23:11.681410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명과세건수과세금액비과세건수비과세금액
과세년도1.0000.0000.0000.0000.0000.000
세목명0.0001.0001.0000.8540.8640.842
과세건수0.0001.0001.0000.7390.7580.573
과세금액0.0000.8540.7391.0000.5390.953
비과세건수0.0000.8640.7580.5391.0000.837
비과세금액0.0000.8420.5730.9530.8371.000
2023-12-11T08:23:11.774471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명
과세년도1.0000.000
세목명0.0001.000
2023-12-11T08:23:11.848278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세건수과세금액비과세건수비과세금액과세년도세목명
과세건수1.0000.6300.7620.6280.0000.911
과세금액0.6301.0000.4320.4940.0000.575
비과세건수0.7620.4321.0000.9280.0000.605
비과세금액0.6280.4940.9281.0000.0000.608
과세년도0.0000.0000.0000.0001.0000.000
세목명0.9110.5750.6050.6080.0001.000

Missing values

2023-12-11T08:23:09.312562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:23:09.456875image/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
0경상남도거창군488802017취득세121921649139700029613183973000
1경상남도거창군488802017등록세003610634000
2경상남도거창군488802017주민세296759856190007518202327000
3경상남도거창군488802017재산세843185094420000230523492216000
4경상남도거창군488802017자동차세42688105640680006285285852000
5경상남도거창군488802017레저세0000
6경상남도거창군488802017담배소비세109443012100000
7경상남도거창군488802017지방소비세0000
8경상남도거창군488802017등록면허세2257711165650003972170387000
9경상남도거창군488802017도시계획세0000
시도명시군구명자치단체코드과세년도세목명과세건수과세금액비과세건수비과세금액
42경상남도거창군488802020재산세895956175053000291103970316000
43경상남도거창군488802020자동차세45902107710660007664273784000
44경상남도거창군488802020레저세0000
45경상남도거창군488802020담배소비세276413869400000
46경상남도거창군488802020지방소비세6537660000000
47경상남도거창군488802020등록면허세2497912834500003456136481000
48경상남도거창군488802020도시계획세0000
49경상남도거창군488802020지역자원시설세211869439610001499156784000
50경상남도거창군488802020지방소득세14275717555300000
51경상남도거창군488802020교육세1536355377549000957000