Overview

Dataset statistics

Number of variables9
Number of observations32
Missing cells3
Missing cells (%)1.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 KiB
Average record size in memory82.1 B

Variable types

Categorical5
Numeric4

Dataset

Description2017년 세목별 지방세 비과, 감면 금액, 2018년 세목별 지방세 비과, 감면 금액, 2019년 세목별 지방세 비과, 감면 금액, 2020년, 2021년, 2022년 세목별 지방세 비과, 감면 금액에 관한 자료입니다.
Author경상남도 거창군
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15079219

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
비과세금액 is highly overall correlated with 감면금액 and 2 other fieldsHigh correlation
감면금액 is highly overall correlated with 비과세금액 and 3 other fieldsHigh correlation
부과금액 is highly overall correlated with 감면금액 and 1 other fieldsHigh correlation
비과세감면율 is highly overall correlated with 비과세금액 and 2 other fieldsHigh correlation
세목명 is highly overall correlated with 비과세금액 and 3 other fieldsHigh correlation
비과세금액 has 3 (9.4%) missing valuesMissing
비과세금액 has 5 (15.6%) zerosZeros
부과금액 has 3 (9.4%) zerosZeros
비과세감면율 has 7 (21.9%) zerosZeros

Reproduction

Analysis started2023-12-10 22:58:01.651308
Analysis finished2023-12-10 22:58:03.902340
Duration2.25 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size388.0 B
경상남도
32 

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 32
100.0%

Length

2023-12-11T07:58:03.969623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:58:04.096274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 32
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size388.0 B
거창군
32 

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 32
100.0%

Length

2023-12-11T07:58:04.236884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:58:04.346198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
거창군 32
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size388.0 B
48880
32 

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 32
100.0%

Length

2023-12-11T07:58:04.508603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:58:04.651920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48880 32
100.0%

세목명
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size388.0 B
교육세
4 
등록세
4 
재산세
4 
주민세
4 
취득세
4 
Other values (3)
12 

Length

Max length7
Median length3
Mean length3.875
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
교육세 4
12.5%
등록세 4
12.5%
재산세 4
12.5%
주민세 4
12.5%
취득세 4
12.5%
자동차세 4
12.5%
등록면허세 4
12.5%
지역자원시설세 4
12.5%

Length

2023-12-11T07:58:04.801635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:58:04.953764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
교육세 4
12.5%
등록세 4
12.5%
재산세 4
12.5%
주민세 4
12.5%
취득세 4
12.5%
자동차세 4
12.5%
등록면허세 4
12.5%
지역자원시설세 4
12.5%

과세년도
Categorical

Distinct4
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size388.0 B
2017
8 
2018
8 
2019
8 
2020
8 

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 8
25.0%
2018 8
25.0%
2019 8
25.0%
2020 8
25.0%

Length

2023-12-11T07:58:05.100800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:58:05.237382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 8
25.0%
2018 8
25.0%
2019 8
25.0%
2020 8
25.0%

비과세금액
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct25
Distinct (%)86.2%
Missing3
Missing (%)9.4%
Infinite0
Infinite (%)0.0%
Mean5.5232114 × 108
Minimum0
Maximum3.215869 × 109
Zeros5
Zeros (%)15.6%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-11T07:58:05.646511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14660000
median55139000
Q35.43801 × 108
95-th percentile2.9969242 × 109
Maximum3.215869 × 109
Range3.215869 × 109
Interquartile range (IQR)5.39141 × 108

Descriptive statistics

Standard deviation1.0204762 × 109
Coefficient of variation (CV)1.8476138
Kurtosis2.4815382
Mean5.5232114 × 108
Median Absolute Deviation (MAD)55139000
Skewness1.9780478
Sum1.6017313 × 1010
Variance1.0413716 × 1018
MonotonicityNot monotonic
2023-12-11T07:58:05.777117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 5
 
15.6%
185419000 1
 
3.1%
808902000 1
 
3.1%
109292000 1
 
3.1%
4784000 1
 
3.1%
52293000 1
 
3.1%
951174000 1
 
3.1%
4823000 1
 
3.1%
3215869000 1
 
3.1%
110384000 1
 
3.1%
Other values (15) 15
46.9%
(Missing) 3
 
9.4%
ValueCountFrequency (%)
0 5
15.6%
3601000 1
 
3.1%
4628000 1
 
3.1%
4660000 1
 
3.1%
4784000 1
 
3.1%
4823000 1
 
3.1%
23670000 1
 
3.1%
47418000 1
 
3.1%
52293000 1
 
3.1%
52742000 1
 
3.1%
ValueCountFrequency (%)
3215869000 1
3.1%
3053011000 1
3.1%
2912794000 1
3.1%
2691326000 1
3.1%
951174000 1
3.1%
808902000 1
3.1%
798762000 1
3.1%
543801000 1
3.1%
185419000 1
3.1%
173633000 1
3.1%

감면금액
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2976297 × 108
Minimum7000
Maximum2.385683 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-11T07:58:05.917340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7000
5-th percentile8550
Q115055250
median77185500
Q33.52751 × 108
95-th percentile2.3529632 × 109
Maximum2.385683 × 109
Range2.385676 × 109
Interquartile range (IQR)3.3769575 × 108

Descriptive statistics

Standard deviation7.4669043 × 108
Coefficient of variation (CV)1.7374471
Kurtosis2.7799577
Mean4.2976297 × 108
Median Absolute Deviation (MAD)77176500
Skewness2.0092505
Sum1.3752415 × 1010
Variance5.575466 × 1017
MonotonicityNot monotonic
2023-12-11T07:58:06.087938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
9000 2
 
6.2%
8000 1
 
3.1%
716088000 1
 
3.1%
47492000 1
 
3.1%
131697000 1
 
3.1%
221491000 1
 
3.1%
1975018000 1
 
3.1%
17378000 1
 
3.1%
754447000 1
 
3.1%
3025000 1
 
3.1%
Other values (21) 21
65.6%
ValueCountFrequency (%)
7000 1
3.1%
8000 1
3.1%
9000 2
6.2%
2208000 1
3.1%
3025000 1
3.1%
7936000 1
3.1%
10634000 1
3.1%
16529000 1
3.1%
16908000 1
3.1%
16978000 1
3.1%
ValueCountFrequency (%)
2385683000 1
3.1%
2375071000 1
3.1%
2334875000 1
3.1%
1975018000 1
3.1%
800890000 1
3.1%
754447000 1
3.1%
716088000 1
3.1%
711674000 1
3.1%
233110000 1
3.1%
222784000 1
3.1%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1006456 × 109
Minimum0
Maximum1.9181784 × 1010
Zeros3
Zeros (%)9.4%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-11T07:58:06.260504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19.75611 × 108
median3.048281 × 109
Q37.0131518 × 109
95-th percentile1.695723 × 1010
Maximum1.9181784 × 1010
Range1.9181784 × 1010
Interquartile range (IQR)6.0375408 × 109

Descriptive statistics

Standard deviation5.5435425 × 109
Coefficient of variation (CV)1.0868315
Kurtosis0.5271816
Mean5.1006456 × 109
Median Absolute Deviation (MAD)2.3058025 × 109
Skewness1.1953366
Sum1.6322066 × 1011
Variance3.0730863 × 1019
MonotonicityNot monotonic
2023-12-11T07:58:06.426177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 3
 
9.4%
5125702000 1
 
3.1%
5908919000 1
 
3.1%
943961000 1
 
3.1%
1283450000 1
 
3.1%
10771066000 1
 
3.1%
17526581000 1
 
3.1%
1190665000 1
 
3.1%
6175053000 1
 
3.1%
48236000 1
 
3.1%
Other values (20) 20
62.5%
ValueCountFrequency (%)
0 3
9.4%
48236000 1
 
3.1%
765944000 1
 
3.1%
913142000 1
 
3.1%
943961000 1
 
3.1%
945587000 1
 
3.1%
985619000 1
 
3.1%
1091524000 1
 
3.1%
1116565000 1
 
3.1%
1139731000 1
 
3.1%
ValueCountFrequency (%)
19181784000 1
3.1%
17526581000 1
3.1%
16491397000 1
3.1%
12949698000 1
3.1%
10771066000 1
3.1%
10564068000 1
3.1%
10168298000 1
3.1%
9527448000 1
3.1%
6175053000 1
3.1%
5908919000 1
3.1%

비과세감면율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)78.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.0075
Minimum0
Maximum68.55
Zeros7
Zeros (%)21.9%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-11T07:58:06.561731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.37
median9.84
Q317.9225
95-th percentile64.5655
Maximum68.55
Range68.55
Interquartile range (IQR)15.5525

Descriptive statistics

Standard deviation20.439715
Coefficient of variation (CV)1.2768837
Kurtosis2.2159974
Mean16.0075
Median Absolute Deviation (MAD)7.8
Skewness1.7704283
Sum512.24
Variance417.78196
MonotonicityNot monotonic
2023-12-11T07:58:06.681955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0.0 7
21.9%
20.53 2
 
6.2%
22.62 1
 
3.1%
16.61 1
 
3.1%
10.63 1
 
3.1%
2.54 1
 
3.1%
16.7 1
 
3.1%
1.86 1
 
3.1%
64.3 1
 
3.1%
6.27 1
 
3.1%
Other values (15) 15
46.9%
ValueCountFrequency (%)
0.0 7
21.9%
1.86 1
 
3.1%
2.54 1
 
3.1%
2.7 1
 
3.1%
2.71 1
 
3.1%
2.84 1
 
3.1%
3.53 1
 
3.1%
6.27 1
 
3.1%
8.72 1
 
3.1%
9.05 1
 
3.1%
ValueCountFrequency (%)
68.55 1
3.1%
64.89 1
3.1%
64.3 1
3.1%
63.79 1
3.1%
22.62 1
3.1%
20.53 2
6.2%
19.31 1
3.1%
17.46 1
3.1%
17.31 1
3.1%
17.19 1
3.1%

Interactions

2023-12-11T07:58:03.219858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:58:01.933777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:58:02.407465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:58:02.808738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:58:03.317806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:58:02.044265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:58:02.493619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:58:02.899487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:58:03.398055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:58:02.159304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:58:02.585656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:58:02.999376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:58:03.499213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:58:02.307630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:58:02.699521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:58:03.109752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:58:06.773264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도비과세금액감면금액부과금액비과세감면율
세목명1.0000.0000.7460.7540.9230.863
과세년도0.0001.0000.0000.0000.0000.000
비과세금액0.7460.0001.0000.9530.8760.952
감면금액0.7540.0000.9531.0000.7950.875
부과금액0.9230.0000.8760.7951.0000.777
비과세감면율0.8630.0000.9520.8750.7771.000
2023-12-11T07:58:06.885241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명
과세년도1.0000.000
세목명0.0001.000
2023-12-11T07:58:06.978928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비과세금액감면금액부과금액비과세감면율세목명과세년도
비과세금액1.0000.7230.3220.8500.5400.000
감면금액0.7231.0000.6640.6720.5610.000
부과금액0.3220.6641.0000.1800.5690.000
비과세감면율0.8500.6720.1801.0000.7140.000
세목명0.5400.5610.5690.7141.0000.000
과세년도0.0000.0000.0000.0000.0001.000

Missing values

2023-12-11T07:58:03.644820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:58:03.830584image/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

시도명시군구명자치단체코드세목명과세년도비과세금액감면금액부과금액비과세감면율
0경상남도거창군48880교육세20170800051257020000.0
1경상남도거창군48880등록세2017<NA>1063400000.0
2경상남도거창군48880재산세20172691326000800890000509442000068.55
3경상남도거창군48880주민세20171854190001690800098561900020.53
4경상남도거창군48880취득세201780890200023750710001649139700019.31
5경상남도거창군48880자동차세201752742000233110000105640680002.71
6경상남도거창군48880등록면허세20173601000166786000111656500015.26
7경상남도거창군48880지역자원시설세20171022900005498400076594400020.53
8경상남도거창군48880교육세20180900051143980000.0
9경상남도거창군48880등록세2018<NA>793600000.0
시도명시군구명자치단체코드세목명과세년도비과세금액감면금액부과금액비과세감면율
22경상남도거창군48880등록면허세201946600009869700011850220008.72
23경상남도거창군48880지역자원시설세20191103840004768100091314200017.31
24경상남도거창군48880교육세20200700053775490000.0
25경상남도거창군48880등록세202003025000482360006.27
26경상남도거창군48880재산세20203215869000754447000617505300064.3
27경상남도거창군48880주민세202048230001737800011906650001.86
28경상남도거창군48880취득세202095117400019750180001752658100016.7
29경상남도거창군48880자동차세202052293000221491000107710660002.54
30경상남도거창군48880등록면허세20204784000131697000128345000010.63
31경상남도거창군48880지역자원시설세20201092920004749200094396100016.61