Overview

Dataset statistics

Number of variables10
Number of observations131
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.1 KiB
Average record size in memory87.0 B

Variable types

Categorical6
Numeric4

Dataset

Description전남 영광군의 지방세 체납현황에 관련된 데이터로 체납액 규모별 체납 건수를 납세자 유형별로 제공하여 체납정책 수립 시 기초자료로 활용할 수 있음.
Author전라남도 영광군
URLhttps://www.data.go.kr/data/15079831/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
체납건수 is highly overall correlated with 누적체납건수 and 1 other fieldsHigh correlation
체납금액 is highly overall correlated with 누적체납금액High correlation
누적체납건수 is highly overall correlated with 체납건수High correlation
누적체납금액 is highly overall correlated with 체납건수 and 1 other fieldsHigh correlation
체납금액 has unique valuesUnique
누적체납금액 has unique valuesUnique

Reproduction

Analysis started2023-12-12 21:54:54.942697
Analysis finished2023-12-12 21:54:57.247796
Duration2.31 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
전라남도
131 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전라남도 131
100.0%

Length

2023-12-13T06:54:57.302465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:54:57.380452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라남도 131
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
영광군
131 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
영광군 131
100.0%

Length

2023-12-13T06:54:57.474523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:54:57.573392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영광군 131
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
46870
131 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row46870
2nd row46870
3rd row46870
4th row46870
5th row46870

Common Values

ValueCountFrequency (%)
46870 131
100.0%

Length

2023-12-13T06:54:57.681011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:54:57.775030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
46870 131
100.0%

과세년도
Categorical

Distinct5
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2021
35 
2020
31 
2018
24 
2019
24 
2017
17 

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 (%)
2021 35
26.7%
2020 31
23.7%
2018 24
18.3%
2019 24
18.3%
2017 17
13.0%

Length

2023-12-13T06:54:57.861794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:54:57.957249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 35
26.7%
2020 31
23.7%
2018 24
18.3%
2019 24
18.3%
2017 17
13.0%

세목명
Categorical

Distinct6
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
재산세
36 
지방소득세
34 
취득세
23 
자동차세
16 
주민세
16 

Length

Max length5
Median length3
Mean length3.7328244
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row등록면허세
2nd row자동차세
3rd row자동차세
4th row자동차세
5th row재산세

Common Values

ValueCountFrequency (%)
재산세 36
27.5%
지방소득세 34
26.0%
취득세 23
17.6%
자동차세 16
12.2%
주민세 16
12.2%
등록면허세 6
 
4.6%

Length

2023-12-13T06:54:58.082196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:54:58.200598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
재산세 36
27.5%
지방소득세 34
26.0%
취득세 23
17.6%
자동차세 16
12.2%
주민세 16
12.2%
등록면허세 6
 
4.6%

체납액구간
Categorical

Distinct10
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
10만원 미만
30 
10만원~30만원미만
26 
30만원~50만원미만
20 
50만원~1백만원미만
16 
1백만원~3백만원미만
13 
Other values (5)
26 

Length

Max length11
Median length11
Mean length10.076336
Min length7

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st row10만원 미만
2nd row10만원 미만
3rd row10만원~30만원미만
4th row30만원~50만원미만
5th row10만원 미만

Common Values

ValueCountFrequency (%)
10만원 미만 30
22.9%
10만원~30만원미만 26
19.8%
30만원~50만원미만 20
15.3%
50만원~1백만원미만 16
12.2%
1백만원~3백만원미만 13
9.9%
3백만원~5백만원미만 10
 
7.6%
5백만원~1천만원미만 6
 
4.6%
1천만원~3천만원미만 6
 
4.6%
3천만원~5천만원미만 3
 
2.3%
5천만원~1억원미만 1
 
0.8%

Length

2023-12-13T06:54:58.372974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:54:58.517398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10만원 30
18.6%
미만 30
18.6%
10만원~30만원미만 26
16.1%
30만원~50만원미만 20
12.4%
50만원~1백만원미만 16
9.9%
1백만원~3백만원미만 13
8.1%
3백만원~5백만원미만 10
 
6.2%
5백만원~1천만원미만 6
 
3.7%
1천만원~3천만원미만 6
 
3.7%
3천만원~5천만원미만 3
 
1.9%

체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct62
Distinct (%)47.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean239.9542
Minimum1
Maximum4673
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-13T06:54:58.704717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median9
Q345
95-th percentile1579
Maximum4673
Range4672
Interquartile range (IQR)43

Descriptive statistics

Standard deviation719.96711
Coefficient of variation (CV)3.0004356
Kurtosis21.025206
Mean239.9542
Median Absolute Deviation (MAD)8
Skewness4.3793091
Sum31434
Variance518352.64
MonotonicityNot monotonic
2023-12-13T06:54:58.835685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 25
19.1%
3 12
 
9.2%
2 9
 
6.9%
4 7
 
5.3%
5 5
 
3.8%
9 5
 
3.8%
7 4
 
3.1%
10 3
 
2.3%
13 3
 
2.3%
32 2
 
1.5%
Other values (52) 56
42.7%
ValueCountFrequency (%)
1 25
19.1%
2 9
 
6.9%
3 12
9.2%
4 7
 
5.3%
5 5
 
3.8%
6 2
 
1.5%
7 4
 
3.1%
8 1
 
0.8%
9 5
 
3.8%
10 3
 
2.3%
ValueCountFrequency (%)
4673 1
0.8%
4442 1
0.8%
2721 1
0.8%
2580 1
0.8%
2311 1
0.8%
2098 1
0.8%
1710 1
0.8%
1448 1
0.8%
863 1
0.8%
853 1
0.8%

체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct131
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17028939
Minimum104780
Maximum1.153483 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-13T06:54:58.972405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum104780
5-th percentile312390
Q11321160
median6738510
Q324389065
95-th percentile70947735
Maximum1.153483 × 108
Range1.1524352 × 108
Interquartile range (IQR)23067905

Descriptive statistics

Standard deviation23326733
Coefficient of variation (CV)1.3698289
Kurtosis4.6370763
Mean17028939
Median Absolute Deviation (MAD)6179240
Skewness2.1037238
Sum2.230791 × 109
Variance5.4413649 × 1014
MonotonicityNot monotonic
2023-12-13T06:54:59.146395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1160130 1
 
0.8%
6901870 1
 
0.8%
197760 1
 
0.8%
5539070 1
 
0.8%
1079670 1
 
0.8%
4466020 1
 
0.8%
2491970 1
 
0.8%
541190 1
 
0.8%
559270 1
 
0.8%
17639880 1
 
0.8%
Other values (121) 121
92.4%
ValueCountFrequency (%)
104780 1
0.8%
148560 1
0.8%
197760 1
0.8%
225620 1
0.8%
271660 1
0.8%
307550 1
0.8%
312000 1
0.8%
312780 1
0.8%
422590 1
0.8%
442710 1
0.8%
ValueCountFrequency (%)
115348300 1
0.8%
109282990 1
0.8%
97110220 1
0.8%
81151900 1
0.8%
79620140 1
0.8%
73786070 1
0.8%
73436150 1
0.8%
68459320 1
0.8%
60828690 1
0.8%
57247120 1
0.8%

누적체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct73
Distinct (%)55.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean681.07634
Minimum1
Maximum13831
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-13T06:54:59.313486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median17
Q3108
95-th percentile4235.5
Maximum13831
Range13830
Interquartile range (IQR)103

Descriptive statistics

Standard deviation2158.7527
Coefficient of variation (CV)3.1696194
Kurtosis22.622279
Mean681.07634
Median Absolute Deviation (MAD)15
Skewness4.5561798
Sum89221
Variance4660213.4
MonotonicityNot monotonic
2023-12-13T06:54:59.453683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 12
 
9.2%
3 9
 
6.9%
2 6
 
4.6%
4 5
 
3.8%
6 5
 
3.8%
17 5
 
3.8%
14 4
 
3.1%
7 4
 
3.1%
9 4
 
3.1%
8 3
 
2.3%
Other values (63) 74
56.5%
ValueCountFrequency (%)
1 12
9.2%
2 6
4.6%
3 9
6.9%
4 5
3.8%
5 3
 
2.3%
6 5
3.8%
7 4
 
3.1%
8 3
 
2.3%
9 4
 
3.1%
10 3
 
2.3%
ValueCountFrequency (%)
13831 1
0.8%
13829 1
0.8%
9387 1
0.8%
6666 1
0.8%
6478 1
0.8%
6214 1
0.8%
4568 1
0.8%
3903 1
0.8%
2462 1
0.8%
2455 1
0.8%

누적체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct131
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34378064
Minimum197760
Maximum3.7952648 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-13T06:54:59.598399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum197760
5-th percentile971040
Q13511900
median12314360
Q344965025
95-th percentile1.1506421 × 108
Maximum3.7952648 × 108
Range3.7932872 × 108
Interquartile range (IQR)41453125

Descriptive statistics

Standard deviation53542279
Coefficient of variation (CV)1.5574547
Kurtosis16.03575
Mean34378064
Median Absolute Deviation (MAD)10853750
Skewness3.419687
Sum4.5035264 × 109
Variance2.8667756 × 1015
MonotonicityNot monotonic
2023-12-13T06:54:59.733947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1992720 1
 
0.8%
17739520 1
 
0.8%
197760 1
 
0.8%
13585850 1
 
0.8%
4010830 1
 
0.8%
4466020 1
 
0.8%
4551230 1
 
0.8%
3684380 1
 
0.8%
1625440 1
 
0.8%
17639880 1
 
0.8%
Other values (121) 121
92.4%
ValueCountFrequency (%)
197760 1
0.8%
350220 1
0.8%
533460 1
0.8%
540560 1
0.8%
604830 1
0.8%
876650 1
0.8%
917610 1
0.8%
1024470 1
0.8%
1066170 1
0.8%
1214320 1
0.8%
ValueCountFrequency (%)
379526480 1
0.8%
280208720 1
0.8%
187748970 1
0.8%
176016540 1
0.8%
170925730 1
0.8%
117951030 1
0.8%
116978040 1
0.8%
113150380 1
0.8%
107557220 1
0.8%
104676210 1
0.8%

Interactions

2023-12-13T06:54:56.418434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:55.295604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:55.680894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:56.044939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:56.779826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:55.391835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:55.766697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:56.131489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:56.854010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:55.490529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:55.860741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:56.228391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:56.936610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:55.584050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:55.947903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:54:56.311714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:54:59.833267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
과세년도1.0000.0000.0000.0000.1320.0000.145
세목명0.0001.0000.1400.4660.4040.2940.280
체납액구간0.0000.1401.0000.0000.7330.0000.213
체납건수0.0000.4660.0001.0000.6480.9540.694
체납금액0.1320.4040.7330.6481.0000.5870.864
누적체납건수0.0000.2940.0000.9540.5871.0000.838
누적체납금액0.1450.2800.2130.6940.8640.8381.000
2023-12-13T06:54:59.940591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도체납액구간세목명
과세년도1.0000.0000.000
체납액구간0.0001.0000.069
세목명0.0000.0691.000
2023-12-13T06:55:00.043935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
체납건수체납금액누적체납건수누적체납금액과세년도세목명체납액구간
체납건수1.0000.4750.9690.5480.0000.1830.000
체납금액0.4751.0000.3680.9690.0310.2240.306
누적체납건수0.9690.3681.0000.4860.0000.1780.000
누적체납금액0.5480.9690.4861.0000.0820.1720.105
과세년도0.0000.0310.0000.0821.0000.0000.000
세목명0.1830.2240.1780.1720.0001.0000.069
체납액구간0.0000.3060.0000.1050.0000.0691.000

Missing values

2023-12-13T06:54:57.051324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:54:57.200019image/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

시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
0전라남도영광군468702017등록면허세10만원 미만11111601301911992720
1전라남도영광군468702017자동차세10만원 미만216859943065525215210
2전라남도영광군468702017자동차세10만원~30만원미만1001518527026340076150
3전라남도영광군468702017자동차세30만원~50만원미만3103394051728550
4전라남도영광군468702017재산세10만원 미만171018933660456849490870
5전라남도영광군468702017재산세10만원~30만원미만131859690284325510
6전라남도영광군468702017재산세1백만원~3백만원미만1133281022794710
7전라남도영광군468702017재산세30만원~50만원미만3125217062456960
8전라남도영광군468702017재산세3백만원~5백만원미만1326183026914760
9전라남도영광군468702017주민세10만원 미만5719630460159223089450
시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
121전라남도영광군468702021지방소득세3천만원~5천만원미만140100920280130780
122전라남도영광군468702021지방소득세50만원~1백만원미만20135833704330098350
123전라남도영광군468702021지방소득세5백만원~1천만원미만10734361501498318530
124전라남도영광군468702021취득세10만원 미만12601810291532610
125전라남도영광군468702021취득세10만원~30만원미만110478081371890
126전라남도영광군468702021취득세1천만원~3천만원미만242807200242807200
127전라남도영광군468702021취득세30만원~50만원미만130755031214320
128전라남도영광군468702021취득세3백만원~5백만원미만1373371013733710
129전라남도영광군468702021취득세50만원~1백만원미만2127380074945520
130전라남도영광군468702021취득세5천만원~1억원미만160828690160828690