Overview

Dataset statistics

Number of variables10
Number of observations211
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.6 KiB
Average record size in memory85.6 B

Variable types

Categorical5
Numeric4
Text1

Dataset

Description체납액 규모별 체납 건수를 납세자 유형별로 제공 (시도명 시군구명 자치단체코드 과세년도 세목명 체납액구간 체납건수 체납금액 누적체납건수 누적체납금액)
URLhttps://www.data.go.kr/data/15079226/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
과세년도 is highly overall correlated with 체납금액 and 1 other fieldsHigh correlation
체납금액 is highly overall correlated with 과세년도 and 2 other fieldsHigh correlation
누적체납건수 is highly overall correlated with 체납금액 and 1 other fieldsHigh correlation
누적체납금액 is highly overall correlated with 과세년도 and 2 other fieldsHigh correlation
체납금액 has 10 (4.7%) zerosZeros
누적체납건수 has 7 (3.3%) zerosZeros
누적체납금액 has 7 (3.3%) zerosZeros

Reproduction

Analysis started2023-12-12 15:08:57.732125
Analysis finished2023-12-12 15:09:00.049582
Duration2.32 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
경기도
211 

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 211
100.0%

Length

2023-12-13T00:09:00.108572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:09:00.195116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 211
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
양주시
211 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
양주시 211
100.0%

Length

2023-12-13T00:09:00.276786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:09:00.357866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
양주시 211
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
41630
211 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row41630
2nd row41630
3rd row41630
4th row41630
5th row41630

Common Values

ValueCountFrequency (%)
41630 211
100.0%

Length

2023-12-13T00:09:00.460906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:09:00.539886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
41630 211
100.0%

과세년도
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.7583
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-13T00:09:00.616329image/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 deviation1.7191817
Coefficient of variation (CV)0.00085118189
Kurtosis-1.2452912
Mean2019.7583
Median Absolute Deviation (MAD)1
Skewness-0.19297209
Sum426169
Variance2.9555856
MonotonicityIncreasing
2023-12-13T00:09:00.704628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2022 45
21.3%
2021 39
18.5%
2020 35
16.6%
2019 33
15.6%
2018 30
14.2%
2017 29
13.7%
ValueCountFrequency (%)
2017 29
13.7%
2018 30
14.2%
2019 33
15.6%
2020 35
16.6%
2021 39
18.5%
2022 45
21.3%
ValueCountFrequency (%)
2022 45
21.3%
2021 39
18.5%
2020 35
16.6%
2019 33
15.6%
2018 30
14.2%
2017 29
13.7%

세목명
Categorical

Distinct7
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
재산세
50 
지방소득세
47 
취득세
45 
주민세
28 
자동차세
24 
Other values (2)
17 

Length

Max length7
Median length3
Mean length3.8056872
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
재산세 50
23.7%
지방소득세 47
22.3%
취득세 45
21.3%
주민세 28
13.3%
자동차세 24
11.4%
지역자원시설세 9
 
4.3%
등록면허세 8
 
3.8%

Length

2023-12-13T00:09:00.837031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:09:00.947978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
재산세 50
23.7%
지방소득세 47
22.3%
취득세 45
21.3%
주민세 28
13.3%
자동차세 24
11.4%
지역자원시설세 9
 
4.3%
등록면허세 8
 
3.8%

체납액구간
Categorical

Distinct10
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
10만원 미만
41 
10만원~30만원미만
34 
50만원~1백만원미만
32 
30만원~50만원미만
30 
1백만원~3백만원미만
20 
Other values (5)
54 

Length

Max length11
Median length11
Mean length10.127962
Min length6

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 41
19.4%
10만원~30만원미만 34
16.1%
50만원~1백만원미만 32
15.2%
30만원~50만원미만 30
14.2%
1백만원~3백만원미만 20
9.5%
3백만원~5백만원미만 18
8.5%
5백만원~1천만원미만 15
 
7.1%
1천만원~5천만원미만 13
 
6.2%
5천만원~1억원미만 5
 
2.4%
1억원 초과 3
 
1.4%

Length

2023-12-13T00:09:01.058104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:09:01.162248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10만원 41
16.1%
미만 41
16.1%
10만원~30만원미만 34
13.3%
50만원~1백만원미만 32
12.5%
30만원~50만원미만 30
11.8%
1백만원~3백만원미만 20
7.8%
3백만원~5백만원미만 18
7.1%
5백만원~1천만원미만 15
 
5.9%
1천만원~5천만원미만 13
 
5.1%
5천만원~1억원미만 5
 
2.0%
Other values (2) 6
 
2.4%

체납건수
Text

Distinct100
Distinct (%)47.4%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2023-12-13T00:09:01.376360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length1.985782
Min length1

Characters and Unicode

Total characters419
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique71 ?
Unique (%)33.6%

Sample

1st row74
2nd row447
3rd row539
4th row20
5th row3
ValueCountFrequency (%)
1 23
 
10.9%
2 17
 
8.1%
3 14
 
6.6%
0 10
 
4.7%
5 9
 
4.3%
11 6
 
2.8%
4 6
 
2.8%
6 4
 
1.9%
18 4
 
1.9%
12 3
 
1.4%
Other values (90) 115
54.5%
2023-12-13T00:09:01.729430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 87
20.8%
2 63
15.0%
3 51
12.2%
5 37
8.8%
4 34
 
8.1%
6 34
 
8.1%
0 29
 
6.9%
8 27
 
6.4%
9 25
 
6.0%
7 18
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 405
96.7%
Other Punctuation 14
 
3.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 87
21.5%
2 63
15.6%
3 51
12.6%
5 37
9.1%
4 34
 
8.4%
6 34
 
8.4%
0 29
 
7.2%
8 27
 
6.7%
9 25
 
6.2%
7 18
 
4.4%
Other Punctuation
ValueCountFrequency (%)
, 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 419
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 87
20.8%
2 63
15.0%
3 51
12.2%
5 37
8.8%
4 34
 
8.1%
6 34
 
8.1%
0 29
 
6.9%
8 27
 
6.4%
9 25
 
6.0%
7 18
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 419
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 87
20.8%
2 63
15.0%
3 51
12.2%
5 37
8.8%
4 34
 
8.1%
6 34
 
8.1%
0 29
 
6.9%
8 27
 
6.4%
9 25
 
6.0%
7 18
 
4.3%

체납금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct194
Distinct (%)91.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67615358
Minimum0
Maximum1.567164 × 109
Zeros10
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-13T00:09:01.873468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile96680
Q12274790
median15321470
Q361957145
95-th percentile3.3129208 × 108
Maximum1.567164 × 109
Range1.567164 × 109
Interquartile range (IQR)59682355

Descriptive statistics

Standard deviation1.6785236 × 108
Coefficient of variation (CV)2.482459
Kurtosis46.325907
Mean67615358
Median Absolute Deviation (MAD)14843200
Skewness6.0899261
Sum1.4266841 × 1010
Variance2.8174413 × 1016
MonotonicityNot monotonic
2023-12-13T00:09:02.010456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10
 
4.7%
71130230 2
 
0.9%
57140950 2
 
0.9%
72376780 2
 
0.9%
25698360 2
 
0.9%
43303340 2
 
0.9%
136661320 2
 
0.9%
68649130 2
 
0.9%
84011650 2
 
0.9%
82709140 1
 
0.5%
Other values (184) 184
87.2%
ValueCountFrequency (%)
0 10
4.7%
93360 1
 
0.5%
100000 1
 
0.5%
113300 1
 
0.5%
116800 1
 
0.5%
123600 1
 
0.5%
153080 1
 
0.5%
230090 1
 
0.5%
238340 1
 
0.5%
276630 1
 
0.5%
ValueCountFrequency (%)
1567164020 1
0.5%
1352469580 1
0.5%
633496950 1
0.5%
507159970 1
0.5%
407137890 1
0.5%
385470410 1
0.5%
383798340 1
0.5%
381159460 1
0.5%
367494440 1
0.5%
346691590 1
0.5%

누적체납건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct117
Distinct (%)55.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean666.43602
Minimum0
Maximum19280
Zeros7
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-13T00:09:02.143017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median26
Q3204
95-th percentile3567.5
Maximum19280
Range19280
Interquartile range (IQR)199

Descriptive statistics

Standard deviation2162.201
Coefficient of variation (CV)3.244424
Kurtosis36.523745
Mean666.43602
Median Absolute Deviation (MAD)25
Skewness5.5228362
Sum140618
Variance4675113.4
MonotonicityNot monotonic
2023-12-13T00:09:02.272356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 18
 
8.5%
2 11
 
5.2%
3 10
 
4.7%
6 8
 
3.8%
13 7
 
3.3%
9 7
 
3.3%
0 7
 
3.3%
4 6
 
2.8%
8 5
 
2.4%
10 4
 
1.9%
Other values (107) 128
60.7%
ValueCountFrequency (%)
0 7
 
3.3%
1 18
8.5%
2 11
5.2%
3 10
4.7%
4 6
 
2.8%
5 3
 
1.4%
6 8
3.8%
7 2
 
0.9%
8 5
 
2.4%
9 7
 
3.3%
ValueCountFrequency (%)
19280 1
0.5%
14513 1
0.5%
9886 1
0.5%
9595 1
0.5%
8305 1
0.5%
6341 1
0.5%
5626 1
0.5%
4789 1
0.5%
4611 1
0.5%
4505 1
0.5%

누적체납금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct201
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0919391 × 108
Minimum0
Maximum2.51646 × 109
Zeros7
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-13T00:09:02.401862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile243395
Q15204110
median22455740
Q31.0538998 × 108
95-th percentile4.9470737 × 108
Maximum2.51646 × 109
Range2.51646 × 109
Interquartile range (IQR)1.0018588 × 108

Descriptive statistics

Standard deviation2.4864544 × 108
Coefficient of variation (CV)2.2770999
Kurtosis47.216951
Mean1.0919391 × 108
Median Absolute Deviation (MAD)21603330
Skewness5.8834768
Sum2.3039915 × 1010
Variance6.1824556 × 1016
MonotonicityNot monotonic
2023-12-13T00:09:02.836825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7
 
3.3%
13143910 4
 
1.9%
1867120 2
 
0.9%
435348810 1
 
0.5%
186981510 1
 
0.5%
23454870 1
 
0.5%
7397250 1
 
0.5%
23869910 1
 
0.5%
23758540 1
 
0.5%
7557450 1
 
0.5%
Other values (191) 191
90.5%
ValueCountFrequency (%)
0 7
3.3%
116800 1
 
0.5%
123600 1
 
0.5%
153080 1
 
0.5%
210160 1
 
0.5%
276630 1
 
0.5%
310160 1
 
0.5%
333330 1
 
0.5%
376320 1
 
0.5%
453280 1
 
0.5%
ValueCountFrequency (%)
2516460030 1
0.5%
1563550090 1
0.5%
949480450 1
0.5%
773597740 1
0.5%
678173790 1
0.5%
633496950 1
0.5%
590054590 1
0.5%
565682110 1
0.5%
541729240 1
0.5%
507296900 1
0.5%

Interactions

2023-12-13T00:08:59.460848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:08:58.092501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:08:58.550775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:08:58.996095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:08:59.563624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:08:58.182941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:08:58.656028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:08:59.095815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:08:59.664110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:08:58.326337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:08:58.776579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:08:59.230575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:08:59.747800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:08:58.448977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:08:58.901185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:08:59.353171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:09:02.922112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
과세년도1.0000.0000.0000.0000.2720.1170.307
세목명0.0001.0000.2400.7850.0000.1690.000
체납액구간0.0000.2401.0000.0000.5330.0690.197
체납건수0.0000.7850.0001.0000.8510.9990.981
체납금액0.2720.0000.5330.8511.0000.6460.887
누적체납건수0.1170.1690.0690.9990.6461.0000.711
누적체납금액0.3070.0000.1970.9810.8870.7111.000
2023-12-13T00:09:03.019671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
체납액구간세목명
체납액구간1.0000.122
세목명0.1221.000
2023-12-13T00:09:03.105206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도체납금액누적체납건수누적체납금액세목명체납액구간
과세년도1.0000.5070.2710.5350.0000.000
체납금액0.5071.0000.5320.9700.0000.303
누적체납건수0.2710.5321.0000.5860.0900.029
누적체납금액0.5350.9700.5861.0000.0000.102
세목명0.0000.0000.0900.0001.0000.122
체납액구간0.0000.3030.0290.1020.1221.000

Missing values

2023-12-13T00:08:59.863865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:08:59.988842image/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
0경기도양주시416302017등록면허세10만원 미만7493432074934320
1경기도양주시416302017자동차세10만원 미만4471898316044718983160
2경기도양주시416302017자동차세10만원~30만원미만5398690360053986903600
3경기도양주시416302017자동차세30만원~50만원미만206728320206728320
4경기도양주시416302017자동차세50만원~1백만원미만3180205031802050
5경기도양주시416302017재산세10만원 미만3021236669030212366690
6경기도양주시416302017재산세10만원~30만원미만93148709409314870940
7경기도양주시416302017재산세30만원~50만원미만3107337031073370
8경기도양주시416302017재산세50만원~1백만원미만9661758096617580
9경기도양주시416302017재산세1백만원~3백만원미만4808396048083960
시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
201경기도양주시416302022취득세10만원 미만15753860281532450
202경기도양주시416302022취득세10만원~30만원미만112073240377289500
203경기도양주시416302022취득세30만원~50만원미만124807210187186740
204경기도양주시416302022취득세50만원~1백만원미만1181840202215740290
205경기도양주시416302022취득세1백만원~3백만원미만30586981404381020110
206경기도양주시416302022취득세3백만원~5백만원미만7267370401350365860
207경기도양주시416302022취득세5백만원~1천만원미만639973170960658930
208경기도양주시416302022취득세1천만원~5천만원미만1838115946026541729240
209경기도양주시416302022취득세5천만원~1억원미만53466915906400090040
210경기도양주시416302022취득세1억원 초과23674944402367494440