Overview

Dataset statistics

Number of variables8
Number of observations33
Missing cells4
Missing cells (%)1.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 KiB
Average record size in memory73.0 B

Variable types

Categorical3
Numeric4
Text1

Dataset

Description경상남도 하동군의 지방세징수 현황 (연번, 구분, 세목별, 목표액, 부과액, 징수액, 미수액, 징수율 등)의 정보를 제공하고 있습니다
Author경상남도 하동군
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15085755

Alerts

목표액 is highly overall correlated with 부과액 and 2 other fieldsHigh correlation
부과액 is highly overall correlated with 목표액 and 3 other fieldsHigh correlation
징수액 is highly overall correlated with 목표액 and 3 other fieldsHigh correlation
구분 is highly overall correlated with 부과액 and 2 other fieldsHigh correlation
세목별 is highly overall correlated with 목표액 and 3 other fieldsHigh correlation
미수액 has 3 (9.1%) missing valuesMissing
징수율 has 1 (3.0%) missing valuesMissing
목표액 has unique valuesUnique
부과액 has unique valuesUnique
징수액 has unique valuesUnique

Reproduction

Analysis started2023-12-11 00:05:41.073928
Analysis finished2023-12-11 00:05:43.443539
Duration2.37 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Categorical

Distinct3
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size396.0 B
2019
11 
2020
11 
2021
11 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2019 11
33.3%
2020 11
33.3%
2021 11
33.3%

Length

2023-12-11T09:05:43.532742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:05:43.634981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 11
33.3%
2020 11
33.3%
2021 11
33.3%

구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Memory size396.0 B
군세
18 
도세
15 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
군세 18
54.5%
도세 15
45.5%

Length

2023-12-11T09:05:43.758962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:05:43.880960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
군세 18
54.5%
도세 15
45.5%

세목별
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)30.3%
Missing0
Missing (%)0.0%
Memory size396.0 B
과년도수입
6 
취득세
3 
등록면허세
3 
지역자원시설세
3 
지방교육세
3 
Other values (5)
15 

Length

Max length7
Median length5
Mean length4.5454545
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row취득세
2nd row등록면허세
3rd row지역자원시설세
4th row지방교육세
5th row과년도수입

Common Values

ValueCountFrequency (%)
과년도수입 6
18.2%
취득세 3
9.1%
등록면허세 3
9.1%
지역자원시설세 3
9.1%
지방교육세 3
9.1%
주민세 3
9.1%
재산세 3
9.1%
자동차세 3
9.1%
담배소비세 3
9.1%
지방소득세 3
9.1%

Length

2023-12-11T09:05:43.996115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:05:44.147020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
과년도수입 6
18.2%
취득세 3
9.1%
등록면허세 3
9.1%
지역자원시설세 3
9.1%
지방교육세 3
9.1%
주민세 3
9.1%
재산세 3
9.1%
자동차세 3
9.1%
담배소비세 3
9.1%
지방소득세 3
9.1%

목표액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4215.5455
Minimum38
Maximum13187
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-11T09:05:44.315126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum38
5-th percentile118.6
Q11150
median3998
Q35710
95-th percentile11437.8
Maximum13187
Range13149
Interquartile range (IQR)4560

Descriptive statistics

Standard deviation3735.4714
Coefficient of variation (CV)0.88611816
Kurtosis0.14525646
Mean4215.5455
Median Absolute Deviation (MAD)2772
Skewness0.93760357
Sum139113
Variance13953746
MonotonicityNot monotonic
2023-12-11T09:05:44.458462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
10587 1
 
3.0%
4544 1
 
3.0%
2730 1
 
3.0%
4312 1
 
3.0%
350 1
 
3.0%
13187 1
 
3.0%
1277 1
 
3.0%
8697 1
 
3.0%
38 1
 
3.0%
946 1
 
3.0%
Other values (23) 23
69.7%
ValueCountFrequency (%)
38 1
3.0%
76 1
3.0%
147 1
3.0%
200 1
3.0%
350 1
3.0%
450 1
3.0%
946 1
3.0%
970 1
3.0%
1150 1
3.0%
1200 1
3.0%
ValueCountFrequency (%)
13187 1
3.0%
12714 1
3.0%
10587 1
3.0%
10028 1
3.0%
9491 1
3.0%
8697 1
3.0%
6000 1
3.0%
5750 1
3.0%
5710 1
3.0%
5650 1
3.0%

부과액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4342.3636
Minimum-483
Maximum12507
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)3.0%
Memory size429.0 B
2023-12-11T09:05:44.582299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-483
5-th percentile719.8
Q11226
median4031
Q36206
95-th percentile11157.8
Maximum12507
Range12990
Interquartile range (IQR)4980

Descriptive statistics

Standard deviation3352.0715
Coefficient of variation (CV)0.7719463
Kurtosis0.093055279
Mean4342.3636
Median Absolute Deviation (MAD)2634
Skewness0.79519401
Sum143298
Variance11236384
MonotonicityNot monotonic
2023-12-11T09:05:44.721007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
10875 1
 
3.0%
4031 1
 
3.0%
2922 1
 
3.0%
5588 1
 
3.0%
1101 1
 
3.0%
11582 1
 
3.0%
977 1
 
3.0%
7421 1
 
3.0%
1942 1
 
3.0%
1106 1
 
3.0%
Other values (23) 23
69.7%
ValueCountFrequency (%)
-483 1
3.0%
334 1
3.0%
977 1
3.0%
1071 1
3.0%
1101 1
3.0%
1106 1
3.0%
1140 1
3.0%
1223 1
3.0%
1226 1
3.0%
1349 1
3.0%
ValueCountFrequency (%)
12507 1
3.0%
11582 1
3.0%
10875 1
3.0%
8861 1
3.0%
7421 1
3.0%
7047 1
3.0%
6665 1
3.0%
6351 1
3.0%
6206 1
3.0%
5960 1
3.0%

징수액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4046.0303
Minimum-694
Maximum11527
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)3.0%
Memory size429.0 B
2023-12-11T09:05:44.839823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-694
5-th percentile138
Q11133
median3863
Q35977
95-th percentile10808.8
Maximum11527
Range12221
Interquartile range (IQR)4844

Descriptive statistics

Standard deviation3364.1082
Coefficient of variation (CV)0.83145896
Kurtosis-0.28710157
Mean4046.0303
Median Absolute Deviation (MAD)2657
Skewness0.64174368
Sum133519
Variance11317224
MonotonicityNot monotonic
2023-12-11T09:05:45.003247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
10624 1
 
3.0%
3913 1
 
3.0%
2922 1
 
3.0%
5430 1
 
3.0%
144 1
 
3.0%
11527 1
 
3.0%
970 1
 
3.0%
7407 1
 
3.0%
129 1
 
3.0%
1098 1
 
3.0%
Other values (23) 23
69.7%
ValueCountFrequency (%)
-694 1
3.0%
129 1
3.0%
144 1
3.0%
200 1
3.0%
376 1
3.0%
404 1
3.0%
970 1
3.0%
1098 1
3.0%
1133 1
3.0%
1200 1
3.0%
ValueCountFrequency (%)
11527 1
3.0%
11086 1
3.0%
10624 1
3.0%
8839 1
3.0%
7407 1
3.0%
7036 1
3.0%
6366 1
3.0%
6091 1
3.0%
5977 1
3.0%
5657 1
3.0%

미수액
Real number (ℝ)

MISSING 

Distinct29
Distinct (%)96.7%
Missing3
Missing (%)9.1%
Infinite0
Infinite (%)0.0%
Mean478.33333
Minimum6
Maximum6800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-11T09:05:45.137105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile8.9
Q133.25
median163.5
Q3291
95-th percentile1371.05
Maximum6800
Range6794
Interquartile range (IQR)257.75

Descriptive statistics

Standard deviation1241.3238
Coefficient of variation (CV)2.595102
Kurtosis25.22775
Mean478.33333
Median Absolute Deviation (MAD)135
Skewness4.8830655
Sum14350
Variance1540884.8
MonotonicityNot monotonic
2023-12-11T09:05:45.255826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
129 2
 
6.1%
126 1
 
3.0%
537 1
 
3.0%
525 1
 
3.0%
256 1
 
3.0%
211 1
 
3.0%
16 1
 
3.0%
276 1
 
3.0%
116 1
 
3.0%
13 1
 
3.0%
Other values (19) 19
57.6%
(Missing) 3
 
9.1%
ValueCountFrequency (%)
6 1
3.0%
8 1
3.0%
10 1
3.0%
13 1
3.0%
16 1
3.0%
22 1
3.0%
25 1
3.0%
26 1
3.0%
55 1
3.0%
116 1
3.0%
ValueCountFrequency (%)
6800 1
3.0%
1421 1
3.0%
1310 1
3.0%
589 1
3.0%
537 1
3.0%
525 1
3.0%
303 1
3.0%
296 1
3.0%
276 1
3.0%
256 1
3.0%

징수율
Text

MISSING 

Distinct29
Distinct (%)90.6%
Missing1
Missing (%)3.0%
Memory size396.0 B
2023-12-11T09:05:45.431582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.0625
Min length5

Characters and Unicode

Total characters194
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)84.4%

Sample

1st row97.70%
2nd row99.30%
3rd row99.80%
4th row96.40%
5th row59.90%
ValueCountFrequency (%)
100.00 3
 
9.4%
96.30 2
 
6.2%
97.17 1
 
3.1%
97.70 1
 
3.1%
97.56 1
 
3.1%
89.41 1
 
3.1%
95.91 1
 
3.1%
96.04 1
 
3.1%
98.61 1
 
3.1%
6.64 1
 
3.1%
Other values (19) 19
59.4%
2023-12-11T09:05:45.870945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 36
18.6%
. 32
16.5%
% 32
16.5%
0 25
12.9%
1 13
 
6.7%
8 12
 
6.2%
6 10
 
5.2%
4 9
 
4.6%
3 8
 
4.1%
7 8
 
4.1%
Other values (2) 9
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 130
67.0%
Other Punctuation 64
33.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 36
27.7%
0 25
19.2%
1 13
 
10.0%
8 12
 
9.2%
6 10
 
7.7%
4 9
 
6.9%
3 8
 
6.2%
7 8
 
6.2%
5 7
 
5.4%
2 2
 
1.5%
Other Punctuation
ValueCountFrequency (%)
. 32
50.0%
% 32
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 194
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 36
18.6%
. 32
16.5%
% 32
16.5%
0 25
12.9%
1 13
 
6.7%
8 12
 
6.2%
6 10
 
5.2%
4 9
 
4.6%
3 8
 
4.1%
7 8
 
4.1%
Other values (2) 9
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 36
18.6%
. 32
16.5%
% 32
16.5%
0 25
12.9%
1 13
 
6.7%
8 12
 
6.2%
6 10
 
5.2%
4 9
 
4.6%
3 8
 
4.1%
7 8
 
4.1%
Other values (2) 9
 
4.6%

Interactions

2023-12-11T09:05:42.838536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:05:41.471191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:05:41.867083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:05:42.247868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:05:42.914713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:05:41.572532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:05:41.951772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:05:42.602019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:05:42.988544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:05:41.673020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:05:42.047802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:05:42.679132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:05:43.068347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:05:41.769175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:05:42.136837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:05:42.761401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:05:46.017847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도구분세목별목표액부과액징수액미수액징수율
연도1.0000.0000.0000.0000.0000.0000.0680.719
구분0.0001.0000.9760.5860.8330.7230.0561.000
세목별0.0000.9761.0000.8930.9510.9670.0000.975
목표액0.0000.5860.8931.0000.9610.9090.0000.978
부과액0.0000.8330.9510.9611.0000.9430.0000.987
징수액0.0000.7230.9670.9090.9431.0000.0000.980
미수액0.0680.0560.0000.0000.0000.0001.0001.000
징수율0.7191.0000.9750.9780.9870.9801.0001.000
2023-12-11T09:05:46.155945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도세목별구분
연도1.0000.0000.000
세목별0.0001.0000.743
구분0.0000.7431.000
2023-12-11T09:05:46.270410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
목표액부과액징수액미수액연도구분세목별
목표액1.0000.9430.981-0.0800.0000.3910.677
부과액0.9431.0000.9700.0520.0000.5870.636
징수액0.9810.9701.000-0.0840.0000.6290.861
미수액-0.0800.052-0.0841.0000.0170.0000.000
연도0.0000.0000.0000.0171.0000.0000.000
구분0.3910.5870.6290.0000.0001.0000.743
세목별0.6770.6360.8610.0000.0000.7431.000

Missing values

2023-12-11T09:05:43.180071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:05:43.301449image/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.
2023-12-11T09:05:43.396410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

연도구분세목별목표액부과액징수액미수액징수율
02019도세취득세10587108751062425197.70%
12019도세등록면허세94611061098899.30%
22019도세지역자원시설세9491886188392299.80%
32019도세지방교육세37663881374313896.40%
42019도세과년도수입7633420013459.90%
52019군세주민세970134913232698.10%
62019군세재산세43005074488518996.30%
72019군세자동차세56505960565730394.90%
82019군세담배소비세266928102810<NA>100.00%
92019군세지방소득세60006206597722996.30%
연도구분세목별목표액부과액징수액미수액징수율
232021도세등록면허세1277977970699.28%
242021도세지역자원시설세8697742174071399.81%
252021도세지방교육세45444031391311697.07%
262021도세과년도수입3819421292766.64%
272021군세주민세1200122312061698.61%
282021군세재산세49005374516121196.04%
292021군세자동차세57506351609125695.91%
302021군세담배소비세275029402940<NA>100.00%
312021군세지방소득세45005233467952589.41%
322021군세과년도수입450107137653735.11%