Overview

Dataset statistics

Number of variables10
Number of observations387
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory32.6 KiB
Average record size in memory86.3 B

Variable types

Categorical6
Numeric4

Dataset

Description지방세 체납현황을 체납액 규모별로 제공하고 있으며, 과세연도, 세목명, 체납액구간, 체납건수, 체납금액, 누적체납건수로 항목이 구성됨
URLhttps://www.data.go.kr/data/15080597/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 is highly overall correlated with 자치단체코드High correlation
자치단체코드 is highly overall correlated with 시군구명High correlation
체납건수 is highly overall correlated with 누적체납건수High correlation
체납금액 is highly overall correlated with 누적체납금액 and 2 other fieldsHigh correlation
누적체납건수 is highly overall correlated with 체납건수High correlation
누적체납금액 is highly overall correlated with 체납금액 and 2 other fieldsHigh correlation
세목명 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

Reproduction

Analysis started2023-12-12 18:42:31.022768
Analysis finished2023-12-12 18:42:35.622548
Duration4.6 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
경기도
387 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 387
100.0%

Length

2023-12-13T03:42:35.727843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:42:35.876168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 387
100.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
성남시분당구
142 
성남시수정구
126 
성남시중원구
119 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row성남시수정구
2nd row성남시중원구
3rd row성남시분당구
4th row성남시수정구
5th row성남시중원구

Common Values

ValueCountFrequency (%)
성남시분당구 142
36.7%
성남시수정구 126
32.6%
성남시중원구 119
30.7%

Length

2023-12-13T03:42:36.052945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:42:36.206767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
성남시분당구 142
36.7%
성남시수정구 126
32.6%
성남시중원구 119
30.7%

자치단체코드
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
41135
142 
41131
126 
41133
119 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row41131
2nd row41133
3rd row41135
4th row41131
5th row41133

Common Values

ValueCountFrequency (%)
41135 142
36.7%
41131 126
32.6%
41133 119
30.7%

Length

2023-12-13T03:42:36.379053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:42:36.548190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
41135 142
36.7%
41131 126
32.6%
41133 119
30.7%

과세년도
Categorical

Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
2022
122 
2021
121 
2017
91 
2019
32 
2018
21 

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 (%)
2022 122
31.5%
2021 121
31.3%
2017 91
23.5%
2019 32
 
8.3%
2018 21
 
5.4%

Length

2023-12-13T03:42:36.738216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:42:36.933093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 122
31.5%
2021 121
31.3%
2017 91
23.5%
2019 32
 
8.3%
2018 21
 
5.4%

세목명
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
지방소득세
105 
재산세
96 
취득세
72 
자동차세
48 
주민세
40 
Other values (3)
26 

Length

Max length7
Median length3
Mean length3.8165375
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지방소득세 105
27.1%
재산세 96
24.8%
취득세 72
18.6%
자동차세 48
12.4%
주민세 40
 
10.3%
등록면허세 21
 
5.4%
지역자원시설세 3
 
0.8%
담배소비세 2
 
0.5%

Length

2023-12-13T03:42:37.151719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:42:37.391148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지방소득세 105
27.1%
재산세 96
24.8%
취득세 72
18.6%
자동차세 48
12.4%
주민세 40
 
10.3%
등록면허세 21
 
5.4%
지역자원시설세 3
 
0.8%
담배소비세 2
 
0.5%

체납액구간
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
10만원~30만원미만
56 
10만원미만
50 
30만원~50만원미만
49 
50만원~1백만원미만
44 
1백만원~3백만원미만
40 
Other values (10)
148 

Length

Max length11
Median length11
Mean length10.018088
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10만원미만
2nd row10만원미만
3rd row10만원미만
4th row1백만원~3백만원미만
5th row1백만원~3백만원미만

Common Values

ValueCountFrequency (%)
10만원~30만원미만 56
14.5%
10만원미만 50
12.9%
30만원~50만원미만 49
12.7%
50만원~1백만원미만 44
11.4%
1백만원~3백만원미만 40
10.3%
1천만원~3천만원미만 31
8.0%
5백만원~1천만원미만 26
6.7%
3백만원~5백만원미만 23
5.9%
10만원 미만 19
 
4.9%
3천만원~5천만원미만 16
 
4.1%
Other values (5) 33
8.5%

Length

2023-12-13T03:42:37.629417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10만원~30만원미만 56
13.8%
10만원미만 50
12.3%
30만원~50만원미만 49
12.1%
50만원~1백만원미만 44
10.8%
1백만원~3백만원미만 40
9.9%
1천만원~3천만원미만 31
7.6%
5백만원~1천만원미만 26
6.4%
3백만원~5백만원미만 23
5.7%
10만원 19
 
4.7%
미만 19
 
4.7%
Other values (6) 49
12.1%

체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct163
Distinct (%)42.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean454.14987
Minimum1
Maximum15693
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-13T03:42:37.839855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median11
Q3192
95-th percentile1870.8
Maximum15693
Range15692
Interquartile range (IQR)189

Descriptive statistics

Standard deviation1610.9785
Coefficient of variation (CV)3.5472398
Kurtosis50.085212
Mean454.14987
Median Absolute Deviation (MAD)10
Skewness6.7433227
Sum175756
Variance2595251.7
MonotonicityNot monotonic
2023-12-13T03:42:38.087829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 53
 
13.7%
2 34
 
8.8%
3 27
 
7.0%
6 17
 
4.4%
5 13
 
3.4%
10 13
 
3.4%
4 13
 
3.4%
7 9
 
2.3%
9 8
 
2.1%
12 5
 
1.3%
Other values (153) 195
50.4%
ValueCountFrequency (%)
1 53
13.7%
2 34
8.8%
3 27
7.0%
4 13
 
3.4%
5 13
 
3.4%
6 17
 
4.4%
7 9
 
2.3%
8 5
 
1.3%
9 8
 
2.1%
10 13
 
3.4%
ValueCountFrequency (%)
15693 1
0.3%
12638 1
0.3%
12372 1
0.3%
12156 1
0.3%
10912 1
0.3%
10109 1
0.3%
4204 1
0.3%
3190 1
0.3%
2656 1
0.3%
2473 1
0.3%

체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct387
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9333523 × 108
Minimum9020
Maximum2.4776314 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-13T03:42:38.379993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9020
5-th percentile316869
Q15811525
median40474010
Q31.323262 × 108
95-th percentile4.873318 × 108
Maximum2.4776314 × 1010
Range2.4776305 × 1010
Interquartile range (IQR)1.2651468 × 108

Descriptive statistics

Standard deviation1.3012039 × 109
Coefficient of variation (CV)6.7302991
Kurtosis332.88416
Mean1.9333523 × 108
Median Absolute Deviation (MAD)38856590
Skewness17.749665
Sum7.4820734 × 1010
Variance1.6931316 × 1018
MonotonicityNot monotonic
2023-12-13T03:42:38.638830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5928640 1
 
0.3%
461270 1
 
0.3%
23405070 1
 
0.3%
41441790 1
 
0.3%
15660790 1
 
0.3%
3262740 1
 
0.3%
2106490 1
 
0.3%
5024620 1
 
0.3%
114783010 1
 
0.3%
39055930 1
 
0.3%
Other values (377) 377
97.4%
ValueCountFrequency (%)
9020 1
0.3%
10500 1
0.3%
72150 1
0.3%
83090 1
0.3%
108870 1
0.3%
109030 1
0.3%
117890 1
0.3%
120970 1
0.3%
174960 1
0.3%
194790 1
0.3%
ValueCountFrequency (%)
24776314480 1
0.3%
5331062370 1
0.3%
2913951740 1
0.3%
1421816890 1
0.3%
1313841170 1
0.3%
1211052930 1
0.3%
865278860 1
0.3%
801735830 1
0.3%
721903500 1
0.3%
691886100 1
0.3%

누적체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct120
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3733.1085
Minimum1
Maximum95247
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-13T03:42:38.932255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.3
Q139.5
median119
Q31235
95-th percentile17531
Maximum95247
Range95246
Interquartile range (IQR)1195.5

Descriptive statistics

Standard deviation11769.616
Coefficient of variation (CV)3.1527657
Kurtosis39.744282
Mean3733.1085
Median Absolute Deviation (MAD)116
Skewness5.9290795
Sum1444713
Variance1.3852387 × 108
MonotonicityNot monotonic
2023-12-13T03:42:39.213028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 9
 
2.3%
85 9
 
2.3%
5 8
 
2.1%
19 8
 
2.1%
3 8
 
2.1%
6 7
 
1.8%
1 6
 
1.6%
2 6
 
1.6%
101 6
 
1.6%
75 6
 
1.6%
Other values (110) 314
81.1%
ValueCountFrequency (%)
1 6
1.6%
2 6
1.6%
3 8
2.1%
4 2
 
0.5%
5 8
2.1%
6 7
1.8%
8 1
 
0.3%
9 2
 
0.5%
11 4
1.0%
13 1
 
0.3%
ValueCountFrequency (%)
95247 3
0.8%
79604 3
0.8%
27587 3
0.8%
20763 3
0.8%
19889 3
0.8%
18221 3
0.8%
17531 3
0.8%
17294 3
0.8%
16863 3
0.8%
15781 3
0.8%

누적체납금액
Real number (ℝ)

HIGH CORRELATION 

Distinct161
Distinct (%)41.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1455265 × 108
Minimum14310
Maximum2.4776314 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2023-12-13T03:42:39.506468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14310
5-th percentile5255560
Q171877955
median3.0934612 × 108
Q38.7805614 × 108
95-th percentile3.0324684 × 109
Maximum2.4776314 × 1010
Range2.47763 × 1010
Interquartile range (IQR)8.0617818 × 108

Descriptive statistics

Standard deviation1.6115246 × 109
Coefficient of variation (CV)1.9784167
Kurtosis126.9864
Mean8.1455265 × 108
Median Absolute Deviation (MAD)2.8268935 × 108
Skewness9.1349566
Sum3.1523188 × 1011
Variance2.5970116 × 1018
MonotonicityNot monotonic
2023-12-13T03:42:39.787464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62749970 3
 
0.8%
588837460 3
 
0.8%
27820170 3
 
0.8%
266160880 3
 
0.8%
239800190 3
 
0.8%
18518900 3
 
0.8%
5169720 3
 
0.8%
2885039950 3
 
0.8%
2423167510 3
 
0.8%
857734960 3
 
0.8%
Other values (151) 357
92.2%
ValueCountFrequency (%)
14310 1
 
0.3%
109030 1
 
0.3%
315140 2
0.5%
622360 1
 
0.3%
1360430 1
 
0.3%
3753050 3
0.8%
4181980 3
0.8%
4395690 3
0.8%
4658250 1
 
0.3%
5169720 3
0.8%
ValueCountFrequency (%)
24776314480 1
 
0.3%
5719239930 1
 
0.3%
5331062370 1
 
0.3%
4811599830 3
0.8%
4566080030 3
0.8%
4424934780 2
0.5%
4119654000 2
0.5%
3527927940 3
0.8%
3397627670 3
0.8%
3032468390 3
0.8%

Interactions

2023-12-13T03:42:34.505088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:42:31.860570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:42:32.656260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:42:33.816059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:42:34.695223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:42:32.059857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:42:32.829442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:42:34.004900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:42:34.856112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:42:32.275259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:42:33.473096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:42:34.162152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:42:34.997348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:42:32.464784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:42:33.642492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:42:34.322990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:42:39.961021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
시군구명1.0001.0000.0000.0000.0000.0000.0000.0000.000
자치단체코드1.0001.0000.0000.0000.0000.0000.0000.0000.000
과세년도0.0000.0001.0000.3440.3950.0860.0000.2540.077
세목명0.0000.0000.3441.0000.5510.3120.8810.4510.851
체납액구간0.0000.0000.3950.5511.0000.3730.8190.6210.789
체납건수0.0000.0000.0860.3120.3731.0000.0000.8840.040
체납금액0.0000.0000.0000.8810.8190.0001.0000.0000.982
누적체납건수0.0000.0000.2540.4510.6210.8840.0001.0000.093
누적체납금액0.0000.0000.0770.8510.7890.0400.9820.0931.000
2023-12-13T03:42:40.157475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도체납액구간시군구명자치단체코드세목명
과세년도1.0000.1760.0000.0000.218
체납액구간0.1761.0000.0000.0000.271
시군구명0.0000.0001.0001.0000.000
자치단체코드0.0000.0001.0001.0000.000
세목명0.2180.2710.0000.0001.000
2023-12-13T03:42:40.350039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
체납건수체납금액누적체납건수누적체납금액시군구명자치단체코드과세년도세목명체납액구간
체납건수1.0000.4300.9200.4530.0000.0000.0550.1720.176
체납금액0.4301.0000.2290.9240.0000.0000.0000.5660.619
누적체납건수0.9200.2291.0000.3500.0000.0000.0970.2960.310
누적체납금액0.4530.9240.3501.0000.0000.0000.0630.5240.578
시군구명0.0000.0000.0000.0001.0001.0000.0000.0000.000
자치단체코드0.0000.0000.0000.0001.0001.0000.0000.0000.000
과세년도0.0550.0000.0970.0630.0000.0001.0000.2180.176
세목명0.1720.5660.2960.5240.0000.0000.2181.0000.271
체납액구간0.1760.6190.3100.5780.0000.0000.1760.2711.000

Missing values

2023-12-13T03:42:35.228510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:42:35.510047image/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
0경기도성남시수정구411312017등록면허세10만원미만1765928640191862749970
1경기도성남시중원구411332017등록면허세10만원미만35912220760191862749970
2경기도성남시분당구411352017등록면허세10만원미만2147192940191862749970
3경기도성남시수정구411312017등록면허세1백만원~3백만원미만1203523035255560
4경기도성남시중원구411332017등록면허세1백만원~3백만원미만2322033035255560
5경기도성남시수정구411312017자동차세10만원미만7443247686011845541500410
6경기도성남시중원구411332017자동차세10만원미만7543222608011845541500410
7경기도성남시분당구411352017자동차세10만원미만3011430330011845541500410
8경기도성남시수정구411312017자동차세10만원~30만원미만88214417172096291552865020
9경기도성남시중원구411332017자동차세10만원~30만원미만83313302577096291552865020
시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
377경기도성남시분당구411352022취득세30만원~50만원미만1388960218191720
378경기도성남시중원구411332022취득세3백만원~5백만원미만138588401138633460
379경기도성남시분당구411352022취득세3백만원~5백만원미만134602301138633460
380경기도성남시수정구411312022취득세3천만원~5천만원미만2671805505185441080
381경기도성남시수정구411312022취득세50만원~1백만원미만19404903121806870
382경기도성남시중원구411332022취득세50만원~1백만원미만326562103121806870
383경기도성남시분당구411352022취득세50만원~1백만원미만425830603121806870
384경기도성남시수정구411312022취득세5백만원~1천만원미만2110065201492500650
385경기도성남시분당구411352022취득세5백만원~1천만원미만5329259401492500650
386경기도성남시수정구411312022취득세5천만원~1억원미만1547239006411141960