Overview

Dataset statistics

Number of variables10
Number of observations162
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.7 KiB
Average record size in memory86.8 B

Variable types

Categorical5
Numeric5

Dataset

Description본 데이터는 경상남도 합천군의 년도별 지방세 체납현황으로, 세목명, 체납액구간, 체납건수, 체납금액, 누적체납건수, 누적체납금액 등의 정보를 제공하고 있습니다.
URLhttps://www.data.go.kr/data/15089300/fileData.do

Alerts

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

Reproduction

Analysis started2023-12-12 11:10:36.807172
Analysis finished2023-12-12 11:10:40.991717
Duration4.18 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
경상남도
162 

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 162
100.0%

Length

2023-12-12T20:10:41.077637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:10:41.224775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 162
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
합천군
162 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
합천군 162
100.0%

Length

2023-12-12T20:10:41.400728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:10:41.549963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
합천군 162
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
48890
162 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row48890
2nd row48890
3rd row48890
4th row48890
5th row48890

Common Values

ValueCountFrequency (%)
48890 162
100.0%

Length

2023-12-12T20:10:41.720004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:10:41.877944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48890 162
100.0%

과세년도
Real number (ℝ)

Distinct6
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.7531
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T20:10:42.023405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12019
median2020
Q32021
95-th percentile2022
Maximum2022
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6076746
Coefficient of variation (CV)0.00079597583
Kurtosis-1.0654243
Mean2019.7531
Median Absolute Deviation (MAD)1
Skewness-0.18059024
Sum327200
Variance2.5846177
MonotonicityIncreasing
2023-12-12T20:10:42.173123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2020 33
20.4%
2019 30
18.5%
2021 30
18.5%
2022 29
17.9%
2018 22
13.6%
2017 18
11.1%
ValueCountFrequency (%)
2017 18
11.1%
2018 22
13.6%
2019 30
18.5%
2020 33
20.4%
2021 30
18.5%
2022 29
17.9%
ValueCountFrequency (%)
2022 29
17.9%
2021 30
18.5%
2020 33
20.4%
2019 30
18.5%
2018 22
13.6%
2017 18
11.1%

세목명
Categorical

Distinct7
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
지방소득세
44 
재산세
43 
취득세
26 
주민세
20 
자동차세
18 
Other values (2)
11 

Length

Max length7
Median length3
Mean length3.8271605
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지방소득세 44
27.2%
재산세 43
26.5%
취득세 26
16.0%
주민세 20
12.3%
자동차세 18
11.1%
등록면허세 8
 
4.9%
지역자원시설세 3
 
1.9%

Length

2023-12-12T20:10:42.347797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:10:42.523806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지방소득세 44
27.2%
재산세 43
26.5%
취득세 26
16.0%
주민세 20
12.3%
자동차세 18
11.1%
등록면허세 8
 
4.9%
지역자원시설세 3
 
1.9%

체납액구간
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
10만원 미만
36 
10만원~30만원미만
30 
30만원~50만원미만
24 
1백만원~3백만원미만
20 
50만원~1백만원미만
18 
Other values (5)
34 

Length

Max length11
Median length11
Mean length10.092593
Min length7

Unique

Unique2 ?
Unique (%)1.2%

Sample

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

Common Values

ValueCountFrequency (%)
10만원 미만 36
22.2%
10만원~30만원미만 30
18.5%
30만원~50만원미만 24
14.8%
1백만원~3백만원미만 20
12.3%
50만원~1백만원미만 18
11.1%
3백만원~5백만원미만 12
 
7.4%
1천만원~3천만원미만 11
 
6.8%
5백만원~1천만원미만 9
 
5.6%
5천만원~1억원미만 1
 
0.6%
1억원~3억원미만 1
 
0.6%

Length

2023-12-12T20:10:42.740845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:10:42.918938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10만원 36
18.2%
미만 36
18.2%
10만원~30만원미만 30
15.2%
30만원~50만원미만 24
12.1%
1백만원~3백만원미만 20
10.1%
50만원~1백만원미만 18
9.1%
3백만원~5백만원미만 12
 
6.1%
1천만원~3천만원미만 11
 
5.6%
5백만원~1천만원미만 9
 
4.5%
5천만원~1억원미만 1
 
0.5%

체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct55
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108.00617
Minimum1
Maximum2485
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T20:10:43.124183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q316.75
95-th percentile576.1
Maximum2485
Range2484
Interquartile range (IQR)14.75

Descriptive statistics

Standard deviation363.28543
Coefficient of variation (CV)3.3635617
Kurtosis28.383616
Mean108.00617
Median Absolute Deviation (MAD)4
Skewness5.1199788
Sum17497
Variance131976.3
MonotonicityNot monotonic
2023-12-12T20:10:43.938993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 40
24.7%
2 18
 
11.1%
3 12
 
7.4%
6 10
 
6.2%
4 9
 
5.6%
5 9
 
5.6%
7 4
 
2.5%
8 4
 
2.5%
12 4
 
2.5%
10 3
 
1.9%
Other values (45) 49
30.2%
ValueCountFrequency (%)
1 40
24.7%
2 18
11.1%
3 12
 
7.4%
4 9
 
5.6%
5 9
 
5.6%
6 10
 
6.2%
7 4
 
2.5%
8 4
 
2.5%
9 2
 
1.2%
10 3
 
1.9%
ValueCountFrequency (%)
2485 1
0.6%
2476 1
0.6%
2177 1
0.6%
1135 1
0.6%
1058 1
0.6%
1053 1
0.6%
831 1
0.6%
744 1
0.6%
582 1
0.6%
464 1
0.6%

체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct162
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9854084.7
Minimum21370
Maximum2.358037 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T20:10:44.178097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21370
5-th percentile156830
Q11294750
median4027315
Q311921982
95-th percentile33318590
Maximum2.358037 × 108
Range2.3578233 × 108
Interquartile range (IQR)10627232

Descriptive statistics

Standard deviation21159533
Coefficient of variation (CV)2.1472855
Kurtosis81.57745
Mean9854084.7
Median Absolute Deviation (MAD)3499500
Skewness7.987187
Sum1.5963617 × 109
Variance4.4772583 × 1014
MonotonicityNot monotonic
2023-12-12T20:10:44.418814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
301520 1
 
0.6%
2607380 1
 
0.6%
156000 1
 
0.6%
11798420 1
 
0.6%
32650180 1
 
0.6%
4896240 1
 
0.6%
43319600 1
 
0.6%
29072680 1
 
0.6%
14517190 1
 
0.6%
27365440 1
 
0.6%
Other values (152) 152
93.8%
ValueCountFrequency (%)
21370 1
0.6%
51740 1
0.6%
82250 1
0.6%
87490 1
0.6%
133720 1
0.6%
136130 1
0.6%
145600 1
0.6%
153160 1
0.6%
156000 1
0.6%
172600 1
0.6%
ValueCountFrequency (%)
235803700 1
0.6%
71121270 1
0.6%
55332510 1
0.6%
45390910 1
0.6%
43319600 1
0.6%
41968650 1
0.6%
36395850 1
0.6%
36127440 1
0.6%
33353770 1
0.6%
32650180 1
0.6%

누적체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct69
Distinct (%)42.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean265.82099
Minimum1
Maximum5628
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T20:10:44.634005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13.25
median11
Q343.75
95-th percentile1343.15
Maximum5628
Range5627
Interquartile range (IQR)40.5

Descriptive statistics

Standard deviation849.18108
Coefficient of variation (CV)3.1945599
Kurtosis25.32501
Mean265.82099
Median Absolute Deviation (MAD)9
Skewness4.8283039
Sum43063
Variance721108.51
MonotonicityNot monotonic
2023-12-12T20:10:44.865950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 17
 
10.5%
2 15
 
9.3%
3 9
 
5.6%
4 8
 
4.9%
5 7
 
4.3%
7 5
 
3.1%
10 5
 
3.1%
6 5
 
3.1%
8 5
 
3.1%
20 4
 
2.5%
Other values (59) 82
50.6%
ValueCountFrequency (%)
1 17
10.5%
2 15
9.3%
3 9
5.6%
4 8
4.9%
5 7
4.3%
6 5
 
3.1%
7 5
 
3.1%
8 5
 
3.1%
9 4
 
2.5%
10 5
 
3.1%
ValueCountFrequency (%)
5628 1
0.6%
5523 1
0.6%
5151 1
0.6%
3143 1
0.6%
2424 1
0.6%
2272 1
0.6%
2090 1
0.6%
1946 1
0.6%
1346 1
0.6%
1289 1
0.6%

누적체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct162
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21723018
Minimum21370
Maximum2.358037 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T20:10:45.088140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21370
5-th percentile308165
Q12767912.5
median8292925
Q329601442
95-th percentile87713407
Maximum2.358037 × 108
Range2.3578233 × 108
Interquartile range (IQR)26833530

Descriptive statistics

Standard deviation31196465
Coefficient of variation (CV)1.4361018
Kurtosis14.360184
Mean21723018
Median Absolute Deviation (MAD)7425125
Skewness3.0842921
Sum3.5191289 × 109
Variance9.7321941 × 1014
MonotonicityNot monotonic
2023-12-12T20:10:45.322814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
598620 1
 
0.6%
6892490 1
 
0.6%
156000 1
 
0.6%
35701090 1
 
0.6%
126371390 1
 
0.6%
14957910 1
 
0.6%
90575770 1
 
0.6%
48533750 1
 
0.6%
23399300 1
 
0.6%
88017900 1
 
0.6%
Other values (152) 152
93.8%
ValueCountFrequency (%)
21370 1
0.6%
51740 1
0.6%
82250 1
0.6%
139230 1
0.6%
156000 1
0.6%
172600 1
0.6%
273420 1
0.6%
300180 1
0.6%
304160 1
0.6%
384260 1
0.6%
ValueCountFrequency (%)
235803700 1
0.6%
127050640 1
0.6%
126371390 1
0.6%
93696870 1
0.6%
92722700 1
0.6%
91386800 1
0.6%
90575770 1
0.6%
89752270 1
0.6%
88017900 1
0.6%
81928040 1
0.6%

Interactions

2023-12-12T20:10:39.974957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:10:37.279401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:10:38.027593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:10:38.660931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:10:39.298484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:10:40.094817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:10:37.437092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:10:38.161908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:10:38.786855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:10:39.435634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:10:40.233894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:10:37.567301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:10:38.287217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:10:38.927920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:10:39.586445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:10:40.352481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:10:37.720111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:10:38.414388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:10:39.054785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:10:39.730333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:10:40.488782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:10:37.875113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:10:38.542601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:10:39.172063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:10:39.855246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:10:45.481055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
과세년도1.0000.0000.0000.0000.1830.0000.000
세목명0.0001.0000.1720.2650.0000.6020.202
체납액구간0.0000.1721.0000.0000.9590.0000.739
체납건수0.0000.2650.0001.0000.2120.9710.344
체납금액0.1830.0000.9590.2121.0000.2790.747
누적체납건수0.0000.6020.0000.9710.2791.0000.546
누적체납금액0.0000.2020.7390.3440.7470.5461.000
2023-12-12T20:10:45.672611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
체납액구간세목명
체납액구간1.0000.085
세목명0.0851.000
2023-12-12T20:10:45.835071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도체납건수체납금액누적체납건수누적체납금액세목명체납액구간
과세년도1.0000.1270.1920.1380.2340.0000.000
체납건수0.1271.0000.3570.9460.3800.0940.000
체납금액0.1920.3571.0000.3060.9470.0000.709
누적체납건수0.1380.9460.3061.0000.4150.2460.000
누적체납금액0.2340.3800.9470.4151.0000.1230.501
세목명0.0000.0940.0000.2460.1231.0000.085
체납액구간0.0000.0000.7090.0000.5010.0851.000

Missing values

2023-12-12T20:10:40.697120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:10:40.915980image/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
0경상남도합천군488902017등록면허세10만원 미만2430152053598620
1경상남도합천군488902017자동차세10만원 미만73298022029412850610
2경상남도합천군488902017자동차세10만원~30만원미만791241209025238951330
3경상남도합천군488902017자동차세30만원~50만원미만62201710113967620
4경상남도합천군488902017재산세10만원 미만5828691390134622030300
5경상남도합천군488902017재산세10만원~30만원미만5742220192895250
6경상남도합천군488902017재산세1천만원~3천만원미만116575690116575690
7경상남도합천군488902017재산세30만원~50만원미만14048301404830
8경상남도합천군488902017재산세3백만원~5백만원미만1453936014539360
9경상남도합천군488902017주민세10만원 미만22731584704966571550
시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
152경상남도합천군488902022지방소득세1천만원~3천만원미만111509580227115250
153경상남도합천군488902022지방소득세30만원~50만원미만62096590124633710
154경상남도합천군488902022지방소득세3백만원~5백만원미만13418680725792680
155경상남도합천군488902022지방소득세50만원~1백만원미만749246502316318610
156경상남도합천군488902022지방소득세5백만원~1천만원미만426167300746384940
157경상남도합천군488902022취득세10만원 미만31361307273420
158경상남도합천군488902022취득세10만원~30만원미만1145600101859640
159경상남도합천군488902022취득세1백만원~3백만원미만47512560915441240
160경상남도합천군488902022취득세30만원~50만원미만3141762041892070
161경상남도합천군488902022취득세50만원~1백만원미만3181960053321980