Overview

Dataset statistics

Number of variables10
Number of observations213
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.0 KiB
Average record size in memory86.6 B

Variable types

Categorical5
Numeric5

Dataset

Description2017년부터 2022년 지방세 체납현황에 대한 세목, 체납액구간, 체납건수, 체납금액, 누적체납건수, 누적체납금액 정보를 제공합니다.
Author경상남도 통영시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15078252

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
체납건수(건) is highly overall correlated with 체납금액(원) and 2 other fieldsHigh correlation
체납금액(원) is highly overall correlated with 체납건수(건) and 1 other fieldsHigh correlation
누적체납건수(건) is highly overall correlated with 체납건수(건) and 1 other fieldsHigh correlation
누적체납금액(원) is highly overall correlated with 체납건수(건) and 2 other fieldsHigh correlation
누적체납금액(원) has unique valuesUnique

Reproduction

Analysis started2024-04-20 18:37:40.333674
Analysis finished2024-04-20 18:37:43.731709
Duration3.4 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
경상남도
213 

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 213
100.0%

Length

2024-04-21T03:37:43.790254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T03:37:43.876323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 213
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
통영시
213 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
통영시 213
100.0%

Length

2024-04-21T03:37:43.952008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T03:37:44.026896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
통영시 213
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
48220
213 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row48220
2nd row48220
3rd row48220
4th row48220
5th row48220

Common Values

ValueCountFrequency (%)
48220 213
100.0%

Length

2024-04-21T03:37:44.104702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T03:37:44.179449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48220 213
100.0%

과세년도
Real number (ℝ)

Distinct6
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.5493
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-04-21T03:37:44.252109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018
median2020
Q32021
95-th percentile2022
Maximum2022
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6889379
Coefficient of variation (CV)0.00083629448
Kurtosis-1.2437064
Mean2019.5493
Median Absolute Deviation (MAD)1
Skewness-0.0027998305
Sum430164
Variance2.8525113
MonotonicityIncreasing
2024-04-21T03:37:44.340142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2019 38
17.8%
2018 37
17.4%
2022 37
17.4%
2020 35
16.4%
2021 35
16.4%
2017 31
14.6%
ValueCountFrequency (%)
2017 31
14.6%
2018 37
17.4%
2019 38
17.8%
2020 35
16.4%
2021 35
16.4%
2022 37
17.4%
ValueCountFrequency (%)
2022 37
17.4%
2021 35
16.4%
2020 35
16.4%
2019 38
17.8%
2018 37
17.4%
2017 31
14.6%

세목명
Categorical

Distinct7
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
지방소득세
51 
재산세
50 
취득세
41 
주민세
32 
자동차세
24 
Other values (2)
15 

Length

Max length7
Median length3
Mean length3.8075117
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지방소득세 51
23.9%
재산세 50
23.5%
취득세 41
19.2%
주민세 32
15.0%
자동차세 24
11.3%
지역자원시설세 8
 
3.8%
등록면허세 7
 
3.3%

Length

2024-04-21T03:37:44.468358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T03:37:44.605052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지방소득세 51
23.9%
재산세 50
23.5%
취득세 41
19.2%
주민세 32
15.0%
자동차세 24
11.3%
지역자원시설세 8
 
3.8%
등록면허세 7
 
3.3%

체납액구간
Categorical

Distinct10
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
10만원 미만
42 
10만원~30만원미만
32 
50만원~1백만원미만
31 
30만원~50만원미만
29 
1백만원~3백만원미만
24 
Other values (5)
55 

Length

Max length11
Median length11
Mean length10.192488
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10만원 미만
2nd row10만원 미만
3rd row10만원~30만원미만
4th row30만원~50만원미만
5th row50만원~1백만원미만

Common Values

ValueCountFrequency (%)
10만원 미만 42
19.7%
10만원~30만원미만 32
15.0%
50만원~1백만원미만 31
14.6%
30만원~50만원미만 29
13.6%
1백만원~3백만원미만 24
11.3%
3백만원~5백만원미만 20
9.4%
1천만원~3천만원미만 14
 
6.6%
5백만원~1천만원미만 12
 
5.6%
3천만원~5천만원미만 5
 
2.3%
5천만원~1억원미만 4
 
1.9%

Length

2024-04-21T03:37:44.908940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T03:37:45.021960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10만원 42
16.5%
미만 42
16.5%
10만원~30만원미만 32
12.5%
50만원~1백만원미만 31
12.2%
30만원~50만원미만 29
11.4%
1백만원~3백만원미만 24
9.4%
3백만원~5백만원미만 20
7.8%
1천만원~3천만원미만 14
 
5.5%
5백만원~1천만원미만 12
 
4.7%
3천만원~5천만원미만 5
 
2.0%

체납건수(건)
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)46.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean347.9108
Minimum1
Maximum7215
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-04-21T03:37:45.149817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median11
Q388
95-th percentile1772
Maximum7215
Range7214
Interquartile range (IQR)85

Descriptive statistics

Standard deviation1020.8604
Coefficient of variation (CV)2.9342591
Kurtosis22.295068
Mean347.9108
Median Absolute Deviation (MAD)10
Skewness4.4662534
Sum74105
Variance1042156
MonotonicityNot monotonic
2024-04-21T03:37:45.260204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 31
 
14.6%
2 16
 
7.5%
3 12
 
5.6%
5 10
 
4.7%
9 9
 
4.2%
6 7
 
3.3%
10 7
 
3.3%
4 6
 
2.8%
7 5
 
2.3%
15 4
 
1.9%
Other values (89) 106
49.8%
ValueCountFrequency (%)
1 31
14.6%
2 16
7.5%
3 12
 
5.6%
4 6
 
2.8%
5 10
 
4.7%
6 7
 
3.3%
7 5
 
2.3%
8 2
 
0.9%
9 9
 
4.2%
10 7
 
3.3%
ValueCountFrequency (%)
7215 1
0.5%
6442 1
0.5%
6306 1
0.5%
4315 1
0.5%
4067 1
0.5%
3849 1
0.5%
3728 1
0.5%
2933 1
0.5%
2190 1
0.5%
1921 1
0.5%

체납금액(원)
Real number (ℝ)

HIGH CORRELATION 

Distinct212
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42633316
Minimum11120
Maximum2.8604339 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-04-21T03:37:45.372952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11120
5-th percentile195066
Q13001400
median21475360
Q368253870
95-th percentile1.5059029 × 108
Maximum2.8604339 × 108
Range2.8603227 × 108
Interquartile range (IQR)65252470

Descriptive statistics

Standard deviation54364353
Coefficient of variation (CV)1.2751613
Kurtosis4.3174495
Mean42633316
Median Absolute Deviation (MAD)20010980
Skewness1.9458058
Sum9.0808964 × 109
Variance2.9554829 × 1015
MonotonicityNot monotonic
2024-04-21T03:37:45.485723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
520020 2
 
0.9%
2793020 1
 
0.5%
186337960 1
 
0.5%
455680 1
 
0.5%
10204780 1
 
0.5%
423330 1
 
0.5%
2516490 1
 
0.5%
79229390 1
 
0.5%
12524770 1
 
0.5%
160680 1
 
0.5%
Other values (202) 202
94.8%
ValueCountFrequency (%)
11120 1
0.5%
26590 1
0.5%
34010 1
0.5%
37240 1
0.5%
51640 1
0.5%
70530 1
0.5%
109100 1
0.5%
120080 1
0.5%
146790 1
0.5%
149720 1
0.5%
ValueCountFrequency (%)
286043390 1
0.5%
261823700 1
0.5%
254872650 1
0.5%
235962120 1
0.5%
233852540 1
0.5%
186337960 1
0.5%
184484400 1
0.5%
172059630 1
0.5%
158617220 1
0.5%
155573240 1
0.5%

누적체납건수(건)
Real number (ℝ)

HIGH CORRELATION 

Distinct128
Distinct (%)60.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1040.0423
Minimum1
Maximum21063
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-04-21T03:37:45.599401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q114
median36
Q3241
95-th percentile6497.8
Maximum21063
Range21062
Interquartile range (IQR)227

Descriptive statistics

Standard deviation3016.984
Coefficient of variation (CV)2.9008283
Kurtosis22.076866
Mean1040.0423
Median Absolute Deviation (MAD)31
Skewness4.3938724
Sum221529
Variance9102192.4
MonotonicityNot monotonic
2024-04-21T03:37:45.731345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 8
 
3.8%
15 7
 
3.3%
17 6
 
2.8%
2 6
 
2.8%
9 5
 
2.3%
14 5
 
2.3%
18 4
 
1.9%
21 4
 
1.9%
19 4
 
1.9%
6 4
 
1.9%
Other values (118) 160
75.1%
ValueCountFrequency (%)
1 8
3.8%
2 6
2.8%
3 3
 
1.4%
4 3
 
1.4%
5 3
 
1.4%
6 4
1.9%
7 4
1.9%
8 3
 
1.4%
9 5
2.3%
10 3
 
1.4%
ValueCountFrequency (%)
21063 1
0.5%
20538 1
0.5%
17319 1
0.5%
13848 1
0.5%
9781 1
0.5%
9470 1
0.5%
9264 1
0.5%
9178 1
0.5%
6848 1
0.5%
6671 1
0.5%

누적체납금액(원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct213
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1729324 × 108
Minimum56940
Maximum1.053696 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-04-21T03:37:45.842543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum56940
5-th percentile964246
Q19983910
median59190230
Q31.7109706 × 108
95-th percentile3.6723275 × 108
Maximum1.053696 × 109
Range1.053639 × 109
Interquartile range (IQR)1.6111315 × 108

Descriptive statistics

Standard deviation1.6186746 × 108
Coefficient of variation (CV)1.3800237
Kurtosis11.760798
Mean1.1729324 × 108
Median Absolute Deviation (MAD)53234170
Skewness2.889282
Sum2.4983461 × 1010
Variance2.6201074 × 1016
MonotonicityNot monotonic
2024-04-21T03:37:45.955067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9790150 1
 
0.5%
146290 1
 
0.5%
6223490 1
 
0.5%
27545670 1
 
0.5%
3638840 1
 
0.5%
15273760 1
 
0.5%
79229390 1
 
0.5%
37775510 1
 
0.5%
160680 1
 
0.5%
314483720 1
 
0.5%
Other values (203) 203
95.3%
ValueCountFrequency (%)
56940 1
0.5%
78490 1
0.5%
83530 1
0.5%
94650 1
0.5%
146290 1
0.5%
160680 1
0.5%
273030 1
0.5%
339780 1
0.5%
791360 1
0.5%
900460 1
0.5%
ValueCountFrequency (%)
1053695950 1
0.5%
1006428600 1
0.5%
935063290 1
0.5%
720385210 1
0.5%
486532670 1
0.5%
485662380 1
0.5%
458370430 1
0.5%
408802770 1
0.5%
400363430 1
0.5%
386206770 1
0.5%

Interactions

2024-04-21T03:37:43.154406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:41.584432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:42.098278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:42.460928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:42.818571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:43.227071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:41.710875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:42.175312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:42.530186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:42.886058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:43.302067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:41.893568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:42.249610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:42.607095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:42.953836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:43.379378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:41.964378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:42.323388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:42.681437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:43.024756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:43.443163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:42.029181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:42.392434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:42.748906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:37:43.087854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T03:37:46.044851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명체납액구간체납건수(건)체납금액(원)누적체납건수(건)누적체납금액(원)
과세년도1.0000.0000.0000.0000.1730.0000.209
세목명0.0001.0000.2860.4110.4030.4300.421
체납액구간0.0000.2861.0000.2200.4030.2600.288
체납건수(건)0.0000.4110.2201.0000.5870.9710.715
체납금액(원)0.1730.4030.4030.5871.0000.5790.848
누적체납건수(건)0.0000.4300.2600.9710.5791.0000.756
누적체납금액(원)0.2090.4210.2880.7150.8480.7561.000
2024-04-21T03:37:46.139528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
체납액구간세목명
체납액구간1.0000.147
세목명0.1471.000
2024-04-21T03:37:46.229026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도체납건수(건)체납금액(원)누적체납건수(건)누적체납금액(원)세목명체납액구간
과세년도1.0000.1750.2220.1600.2270.0000.000
체납건수(건)0.1751.0000.5580.9540.5760.2340.105
체납금액(원)0.2220.5581.0000.4720.9680.2250.196
누적체납건수(건)0.1600.9540.4721.0000.5390.2460.126
누적체납금액(원)0.2270.5760.9680.5391.0000.2410.140
세목명0.0000.2340.2250.2460.2411.0000.147
체납액구간0.0000.1050.1960.1260.1400.1471.000

Missing values

2024-04-21T03:37:43.541794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T03:37:43.680631image/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

시도명시군구명자치단체코드과세년도세목명체납액구간체납건수(건)체납금액(원)누적체납건수(건)누적체납금액(원)
0경상남도통영시482202017등록면허세10만원 미만17127930205849790150
1경상남도통영시482202017자동차세10만원 미만602282297202755125653100
2경상남도통영시482202017자동차세10만원~30만원미만6361031832902168342750660
3경상남도통영시482202017자동차세30만원~50만원미만1653836704514811980
4경상남도통영시482202017자동차세50만원~1백만원미만31817440117265760
5경상남도통영시482202017재산세10만원 미만62419216610216759164390
6경상남도통영시482202017재산세10만원~30만원미만1181754252027442407880
7경상남도통영시482202017재산세1백만원~3백만원미만9140632503656671190
8경상남도통영시482202017재산세1천만원~3천만원미만1225324408119259500
9경상남도통영시482202017재산세30만원~50만원미만592382367015763486740
시도명시군구명자치단체코드과세년도세목명체납액구간체납건수(건)체납금액(원)누적체납건수(건)누적체납금액(원)
203경상남도통영시482202022지역자원시설세10만원 미만1027862021339780
204경상남도통영시482202022지역자원시설세10만원~30만원미만22730302273030
205경상남도통영시482202022지역자원시설세50만원~1백만원미만2119000021190000
206경상남도통영시482202022취득세10만원 미만521799022954130
207경상남도통영시482202022취득세10만원~30만원미만4754790325956060
208경상남도통영시482202022취득세1백만원~3백만원미만114643801422554900
209경상남도통영시482202022취득세1천만원~3천만원미만122811140122811140
210경상남도통영시482202022취득세30만원~50만원미만3128024093740430
211경상남도통영시482202022취득세3백만원~5백만원미만1421195027816510
212경상남도통영시482202022취득세50만원~1백만원미만535079202215861000