Overview

Dataset statistics

Number of variables10
Number of observations64
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.5 KiB
Average record size in memory88.1 B

Variable types

Categorical6
Numeric4

Dataset

Description해당자료는 경기도 구리시의 지방세 과세현황 2019년기준 2018년기준 2017년 기 준 현황 자료(시군구명, 자치단체코드, 세목명, 과세건수, 과세금액 등)에 대한 제공 입니다.
URLhttps://www.data.go.kr/data/15080562/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
데이터기준일자 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 12 (18.8%) zerosZeros
과세금액 has 12 (18.8%) zerosZeros
비과세건수 has 25 (39.1%) zerosZeros
비과세금액 has 29 (45.3%) zerosZeros

Reproduction

Analysis started2023-12-12 02:37:25.954491
Analysis finished2023-12-12 02:37:28.817852
Duration2.86 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
경기도
64 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 64
100.0%

Length

2023-12-12T11:37:28.905005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:37:29.011500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 64
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
구리시 64
100.0%

Length

2023-12-12T11:37:29.140512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:37:29.287352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
구리시 64
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size644.0 B
41310
64 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row41310
2nd row41310
3rd row41310
4th row41310
5th row41310

Common Values

ValueCountFrequency (%)
41310 64
100.0%

Length

2023-12-12T11:37:29.397864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:37:29.540848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
41310 64
100.0%

과세년도
Categorical

Distinct5
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Memory size644.0 B
2017
13 
2018
13 
2019
13 
2021
13 
2020
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%
2019 13
20.3%
2021 13
20.3%
2020 12
18.8%

Length

2023-12-12T11:37:29.683480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:37:29.819997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 13
20.3%
2018 13
20.3%
2019 13
20.3%
2021 13
20.3%
2020 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

2023-12-12T11:37:29.969679image/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 

Distinct51
Distinct (%)79.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71519.672
Minimum0
Maximum372827
Zeros12
Zeros (%)18.8%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-12T11:37:30.115885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median57182
Q395495.5
95-th percentile347811.3
Maximum372827
Range372827
Interquartile range (IQR)95485.5

Descriptive statistics

Standard deviation94664.377
Coefficient of variation (CV)1.3236131
Kurtosis4.2040735
Mean71519.672
Median Absolute Deviation (MAD)56806
Skewness2.0716711
Sum4577259
Variance8.9613443 × 109
MonotonicityNot monotonic
2023-12-12T11:37:30.319815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12
 
18.8%
13 2
 
3.1%
3 2
 
3.1%
76346 1
 
1.6%
69909 1
 
1.6%
363621 1
 
1.6%
23622 1
 
1.6%
86702 1
 
1.6%
95436 1
 
1.6%
112530 1
 
1.6%
Other values (41) 41
64.1%
ValueCountFrequency (%)
0 12
18.8%
1 1
 
1.6%
3 2
 
3.1%
4 1
 
1.6%
12 1
 
1.6%
13 2
 
3.1%
84 1
 
1.6%
91 1
 
1.6%
108 1
 
1.6%
272 1
 
1.6%
ValueCountFrequency (%)
372827 1
1.6%
363621 1
1.6%
361351 1
1.6%
351048 1
1.6%
329470 1
1.6%
129729 1
1.6%
128251 1
1.6%
124679 1
1.6%
117901 1
1.6%
112530 1
1.6%

과세금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct53
Distinct (%)82.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9509625 × 1010
Minimum0
Maximum1.1812 × 1011
Zeros12
Zeros (%)18.8%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-12T11:37:30.523007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.0894345 × 109
median6.4300855 × 109
Q32.7204476 × 1010
95-th percentile7.830508 × 1010
Maximum1.1812 × 1011
Range1.1812 × 1011
Interquartile range (IQR)2.4115041 × 1010

Descriptive statistics

Standard deviation2.6491809 × 1010
Coefficient of variation (CV)1.357884
Kurtosis5.0546362
Mean1.9509625 × 1010
Median Absolute Deviation (MAD)6.4300855 × 109
Skewness2.1745662
Sum1.248616 × 1012
Variance7.0181592 × 1020
MonotonicityNot monotonic
2023-12-12T11:37:30.749163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12
 
18.8%
88894081000 1
 
1.6%
34854448000 1
 
1.6%
30916884000 1
 
1.6%
23419323000 1
 
1.6%
116517000000 1
 
1.6%
3529850000 1
 
1.6%
44296294000 1
 
1.6%
28876003000 1
 
1.6%
944620000 1
 
1.6%
Other values (43) 43
67.2%
ValueCountFrequency (%)
0 12
18.8%
270409000 1
 
1.6%
944620000 1
 
1.6%
3006000000 1
 
1.6%
3009987000 1
 
1.6%
3115917000 1
 
1.6%
3145000000 1
 
1.6%
3234779000 1
 
1.6%
3439046000 1
 
1.6%
3529850000 1
 
1.6%
ValueCountFrequency (%)
118120000000 1
1.6%
116517000000 1
1.6%
88894081000 1
1.6%
79469290000 1
1.6%
71707889000 1
1.6%
46834674000 1
1.6%
44296294000 1
1.6%
41725172000 1
1.6%
41088217000 1
1.6%
38570798000 1
1.6%

비과세건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)59.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3303.2031
Minimum0
Maximum23965
Zeros25
Zeros (%)39.1%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-12T11:37:30.921879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median10
Q34738.25
95-th percentile13397.25
Maximum23965
Range23965
Interquartile range (IQR)4738.25

Descriptive statistics

Standard deviation5648.3367
Coefficient of variation (CV)1.7099574
Kurtosis4.1464456
Mean3303.2031
Median Absolute Deviation (MAD)10
Skewness2.097408
Sum211405
Variance31903707
MonotonicityNot monotonic
2023-12-12T11:37:31.094646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 25
39.1%
2 2
 
3.1%
1 2
 
3.1%
4246 1
 
1.6%
4659 1
 
1.6%
6318 1
 
1.6%
22311 1
 
1.6%
13404 1
 
1.6%
1367 1
 
1.6%
417 1
 
1.6%
Other values (28) 28
43.8%
ValueCountFrequency (%)
0 25
39.1%
1 2
 
3.1%
2 2
 
3.1%
3 1
 
1.6%
4 1
 
1.6%
8 1
 
1.6%
12 1
 
1.6%
29 1
 
1.6%
397 1
 
1.6%
417 1
 
1.6%
ValueCountFrequency (%)
23965 1
1.6%
22311 1
1.6%
19491 1
1.6%
13404 1
1.6%
13359 1
1.6%
12550 1
1.6%
11820 1
1.6%
10583 1
1.6%
9638 1
1.6%
9361 1
1.6%

비과세금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)56.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1257958 × 109
Minimum0
Maximum2.6396857 × 1010
Zeros29
Zeros (%)45.3%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-12T11:37:31.283618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5690000
Q34.4572675 × 108
95-th percentile2.0917227 × 1010
Maximum2.6396857 × 1010
Range2.6396857 × 1010
Interquartile range (IQR)4.4572675 × 108

Descriptive statistics

Standard deviation7.205425 × 109
Coefficient of variation (CV)2.305149
Kurtosis3.0493984
Mean3.1257958 × 109
Median Absolute Deviation (MAD)5690000
Skewness2.1453764
Sum2.0005093 × 1011
Variance5.1918149 × 1019
MonotonicityNot monotonic
2023-12-12T11:37:31.445622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 29
45.3%
12976500000 1
 
1.6%
1000 1
 
1.6%
343581000 1
 
1.6%
19343595000 1
 
1.6%
23184000 1
 
1.6%
23789660000 1
 
1.6%
756889000 1
 
1.6%
79013000 1
 
1.6%
355432000 1
 
1.6%
Other values (26) 26
40.6%
ValueCountFrequency (%)
0 29
45.3%
1000 1
 
1.6%
801000 1
 
1.6%
2240000 1
 
1.6%
9140000 1
 
1.6%
14382000 1
 
1.6%
23184000 1
 
1.6%
26654000 1
 
1.6%
47723000 1
 
1.6%
51010000 1
 
1.6%
ValueCountFrequency (%)
26396857000 1
1.6%
23789660000 1
1.6%
21217681000 1
1.6%
21108496000 1
1.6%
19833370000 1
1.6%
19809429000 1
1.6%
19343595000 1
1.6%
14950989000 1
1.6%
13483607000 1
1.6%
12976500000 1
1.6%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size644.0 B
2023-06-21
64 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-06-21
2nd row2023-06-21
3rd row2023-06-21
4th row2023-06-21
5th row2023-06-21

Common Values

ValueCountFrequency (%)
2023-06-21 64
100.0%

Length

2023-12-12T11:37:31.622852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:37:31.737183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-06-21 64
100.0%

Interactions

2023-12-12T11:37:27.569050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:37:26.304950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:37:26.722320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:37:27.105100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:37:27.686738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:37:26.399477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:37:26.818039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:37:27.212918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:37:27.783840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:37:26.490327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:37:26.919342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:37:27.336431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:37:27.881471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:37:26.610179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:37:27.011477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:37:27.429077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:37:31.820688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명과세건수과세금액비과세건수비과세금액
과세년도1.0000.0000.0000.0000.0000.000
세목명0.0001.0000.9190.8440.7410.693
과세건수0.0000.9191.0000.4120.3560.000
과세금액0.0000.8440.4121.0000.6820.890
비과세건수0.0000.7410.3560.6821.0000.791
비과세금액0.0000.6930.0000.8900.7911.000
2023-12-12T11:37:31.954845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명
과세년도1.0000.000
세목명0.0001.000
2023-12-12T11:37:32.051701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세건수과세금액비과세건수비과세금액과세년도세목명
과세건수1.0000.5380.5860.4440.0000.722
과세금액0.5381.0000.4140.4420.0000.568
비과세건수0.5860.4141.0000.9190.0000.431
비과세금액0.4440.4420.9191.0000.0000.403
과세년도0.0000.0000.0000.0001.0000.000
세목명0.7220.5680.4310.4030.0001.000

Missing values

2023-12-12T11:37:28.391389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:37:28.739872image/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

시도명시군구명자치단체코드과세년도세목명과세건수과세금액비과세건수비과세금액데이터기준일자
0경기도구리시413102017취득세20488888940810004935129765000002023-06-21
1경기도구리시413102017등록세004143820002023-06-21
2경기도구리시413102017주민세8236930099870003772477230002023-06-21
3경기도구리시413102017재산세81786353494860007541198333700002023-06-21
4경기도구리시413102017자동차세1052092252355000093617498890002023-06-21
5경기도구리시413102017레저세136577175000002023-06-21
6경기도구리시413102017담배소비세10812279109000002023-06-21
7경기도구리시413102017지방소비세00002023-06-21
8경기도구리시413102017등록면허세52878601561200016832839520002023-06-21
9경기도구리시413102017도시계획세00002023-06-21
시도명시군구명자치단체코드과세년도세목명과세건수과세금액비과세건수비과세금액데이터기준일자
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