Overview

Dataset statistics

Number of variables10
Number of observations149
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.6 KiB
Average record size in memory86.9 B

Variable types

Categorical5
Numeric5

Dataset

Description2017년부터 2022년까지 서천군 지방세 세목별 체납액에 대한 과세자료를 제공합니다.(세목별, 체납액구간 별, 체납건수, 체납금액 포함)
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=347&beforeMenuCd=DOM_000000201001001000&publicdatapk=15080480

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
체납건수 is highly overall correlated with 누적체납건수 High correlation
체납금액 is highly overall correlated with 누적체납금액 High correlation
누적체납건수 is highly overall correlated with 체납건수 High correlation
누적체납금액 is highly overall correlated with 체납금액 High correlation
체납금액 has unique valuesUnique
누적체납금액 has unique valuesUnique

Reproduction

Analysis started2024-01-09 22:35:53.376590
Analysis finished2024-01-09 22:35:56.029088
Duration2.65 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
충청남도
149 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
충청남도 149
100.0%

Length

2024-01-10T07:35:56.083698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:35:56.166107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청남도 149
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
서천군
149 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서천군 149
100.0%

Length

2024-01-10T07:35:56.254659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:35:56.346772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서천군 149
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
44770
149 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row44770
2nd row44770
3rd row44770
4th row44770
5th row44770

Common Values

ValueCountFrequency (%)
44770 149
100.0%

Length

2024-01-10T07:35:56.428195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:35:56.506950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
44770 149
100.0%

과세년도
Real number (ℝ)

Distinct6
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.7651
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-01-10T07:35:56.603742image/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 deviation1.6414778
Coefficient of variation (CV)0.0008127073
Kurtosis-1.1053031
Mean2019.7651
Median Absolute Deviation (MAD)1
Skewness-0.21066958
Sum300945
Variance2.6944495
MonotonicityIncreasing
2024-01-10T07:35:56.710098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2020 30
20.1%
2021 28
18.8%
2022 28
18.8%
2019 25
16.8%
2018 20
13.4%
2017 18
12.1%
ValueCountFrequency (%)
2017 18
12.1%
2018 20
13.4%
2019 25
16.8%
2020 30
20.1%
2021 28
18.8%
2022 28
18.8%
ValueCountFrequency (%)
2022 28
18.8%
2021 28
18.8%
2020 30
20.1%
2019 25
16.8%
2018 20
13.4%
2017 18
12.1%

세목명
Categorical

Distinct6
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
지방소득세
46 
재산세
40 
취득세
24 
자동차세
18 
주민세
14 

Length

Max length5
Median length3
Mean length3.8322148
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지방소득세 46
30.9%
재산세 40
26.8%
취득세 24
16.1%
자동차세 18
 
12.1%
주민세 14
 
9.4%
등록면허세 7
 
4.7%

Length

2024-01-10T07:35:56.834913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:35:56.969090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지방소득세 46
30.9%
재산세 40
26.8%
취득세 24
16.1%
자동차세 18
 
12.1%
주민세 14
 
9.4%
등록면허세 7
 
4.7%

체납액구간
Categorical

Distinct10
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
10만원 미만
33 
10만원~30만원미만
25 
30만원~50만원미만
23 
1백만원~3백만원미만
17 
50만원~1백만원미만
16 
Other values (5)
35 

Length

Max length11
Median length11
Mean length10.09396
Min length7

Unique

Unique1 ?
Unique (%)0.7%

Sample

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

Common Values

ValueCountFrequency (%)
10만원 미만 33
22.1%
10만원~30만원미만 25
16.8%
30만원~50만원미만 23
15.4%
1백만원~3백만원미만 17
11.4%
50만원~1백만원미만 16
10.7%
3백만원~5백만원미만 14
9.4%
5백만원~1천만원미만 10
 
6.7%
1천만원~3천만원미만 7
 
4.7%
5천만원~1억원미만 3
 
2.0%
3천만원~5천만원미만 1
 
0.7%

Length

2024-01-10T07:35:57.099372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:35:57.213081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10만원 33
18.1%
미만 33
18.1%
10만원~30만원미만 25
13.7%
30만원~50만원미만 23
12.6%
1백만원~3백만원미만 17
9.3%
50만원~1백만원미만 16
8.8%
3백만원~5백만원미만 14
7.7%
5백만원~1천만원미만 10
 
5.5%
1천만원~3천만원미만 7
 
3.8%
5천만원~1억원미만 3
 
1.6%

체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct67
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean160.42953
Minimum1
Maximum2960
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-01-10T07:35:57.344320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median8
Q349
95-th percentile542
Maximum2960
Range2959
Interquartile range (IQR)47

Descriptive statistics

Standard deviation481.21383
Coefficient of variation (CV)2.999534
Kurtosis20.220474
Mean160.42953
Median Absolute Deviation (MAD)7
Skewness4.4555988
Sum23904
Variance231566.75
MonotonicityNot monotonic
2024-01-10T07:35:57.461281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 30
20.1%
2 16
 
10.7%
4 8
 
5.4%
8 7
 
4.7%
3 6
 
4.0%
5 5
 
3.4%
19 3
 
2.0%
6 3
 
2.0%
21 3
 
2.0%
24 3
 
2.0%
Other values (57) 65
43.6%
ValueCountFrequency (%)
1 30
20.1%
2 16
10.7%
3 6
 
4.0%
4 8
 
5.4%
5 5
 
3.4%
6 3
 
2.0%
7 1
 
0.7%
8 7
 
4.7%
9 2
 
1.3%
10 2
 
1.3%
ValueCountFrequency (%)
2960 1
0.7%
2820 1
0.7%
2459 1
0.7%
2204 1
0.7%
2171 1
0.7%
1569 1
0.7%
835 1
0.7%
576 1
0.7%
491 1
0.7%
477 1
0.7%

체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct149
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15624245
Minimum83790
Maximum1.1991882 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-01-10T07:35:57.581291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum83790
5-th percentile389032
Q12270850
median6755470
Q320340630
95-th percentile60523882
Maximum1.1991882 × 108
Range1.1983503 × 108
Interquartile range (IQR)18069780

Descriptive statistics

Standard deviation20995521
Coefficient of variation (CV)1.3437783
Kurtosis5.7670212
Mean15624245
Median Absolute Deviation (MAD)5802330
Skewness2.2376595
Sum2.3280125 × 109
Variance4.408119 × 1014
MonotonicityNot monotonic
2024-01-10T07:35:57.713627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
776180 1
 
0.7%
17942080 1
 
0.7%
6362180 1
 
0.7%
61019550 1
 
0.7%
35566870 1
 
0.7%
33981320 1
 
0.7%
15830080 1
 
0.7%
32689990 1
 
0.7%
24079500 1
 
0.7%
12767740 1
 
0.7%
Other values (139) 139
93.3%
ValueCountFrequency (%)
83790 1
0.7%
98840 1
0.7%
111350 1
0.7%
153110 1
0.7%
303850 1
0.7%
333440 1
0.7%
334300 1
0.7%
370760 1
0.7%
416440 1
0.7%
425750 1
0.7%
ValueCountFrequency (%)
119918820 1
0.7%
94744930 1
0.7%
88793280 1
0.7%
82617600 1
0.7%
62385940 1
0.7%
62292440 1
0.7%
61610510 1
0.7%
61019550 1
0.7%
59780380 1
0.7%
56298880 1
0.7%

누적체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct83
Distinct (%)55.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean381.91275
Minimum1
Maximum5676
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-01-10T07:35:57.834709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median20
Q396
95-th percentile2151.6
Maximum5676
Range5675
Interquartile range (IQR)91

Descriptive statistics

Standard deviation995.63163
Coefficient of variation (CV)2.6069609
Kurtosis15.608881
Mean381.91275
Median Absolute Deviation (MAD)18
Skewness3.8108423
Sum56905
Variance991282.34
MonotonicityNot monotonic
2024-01-10T07:35:57.941174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 13
 
8.7%
2 10
 
6.7%
3 8
 
5.4%
5 7
 
4.7%
11 6
 
4.0%
6 5
 
3.4%
9 5
 
3.4%
37 3
 
2.0%
17 3
 
2.0%
10 3
 
2.0%
Other values (73) 86
57.7%
ValueCountFrequency (%)
1 13
8.7%
2 10
6.7%
3 8
5.4%
4 3
 
2.0%
5 7
4.7%
6 5
 
3.4%
7 3
 
2.0%
8 1
 
0.7%
9 5
 
3.4%
10 3
 
2.0%
ValueCountFrequency (%)
5676 1
0.7%
5606 1
0.7%
5596 1
0.7%
3562 1
0.7%
3452 1
0.7%
3217 1
0.7%
3080 1
0.7%
2382 1
0.7%
1806 1
0.7%
1557 1
0.7%

누적체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct149
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32700654
Minimum223860
Maximum2.4546259 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-01-10T07:35:58.059104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum223860
5-th percentile1154736
Q15667380
median15258770
Q346150370
95-th percentile1.0677459 × 108
Maximum2.4546259 × 108
Range2.4523873 × 108
Interquartile range (IQR)40482990

Descriptive statistics

Standard deviation41951190
Coefficient of variation (CV)1.2828854
Kurtosis6.8192052
Mean32700654
Median Absolute Deviation (MAD)13319030
Skewness2.3638604
Sum4.8723975 × 109
Variance1.7599024 × 1015
MonotonicityNot monotonic
2024-01-10T07:35:58.201772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1923090 1
 
0.7%
52325840 1
 
0.7%
18196340 1
 
0.7%
99468230 1
 
0.7%
57734400 1
 
0.7%
55982840 1
 
0.7%
24210340 1
 
0.7%
40960710 1
 
0.7%
36677740 1
 
0.7%
12767740 1
 
0.7%
Other values (139) 139
93.3%
ValueCountFrequency (%)
223860 1
0.7%
247020 1
0.7%
303850 1
0.7%
377470 1
0.7%
579880 1
0.7%
662210 1
0.7%
726700 1
0.7%
1100520 1
0.7%
1236060 1
0.7%
1237690 1
0.7%
ValueCountFrequency (%)
245462590 1
0.7%
196352360 1
0.7%
183852080 1
0.7%
172636570 1
0.7%
157130870 1
0.7%
143283390 1
0.7%
127553200 1
0.7%
107185050 1
0.7%
106158900 1
0.7%
99468230 1
0.7%

Interactions

2024-01-10T07:35:55.232794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:35:53.632226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:35:54.060936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:35:54.458207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:35:54.868543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:35:55.303569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:35:53.712111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:35:54.134567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:35:54.533418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:35:54.946719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:35:55.375430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:35:53.806543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:35:54.213356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:35:54.607692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:35:55.016612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:35:55.685015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:35:53.900455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:35:54.299578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:35:54.686593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:35:55.093115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:35:55.752913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:35:53.984624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:35:54.383975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:35:54.774555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:35:55.159278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T07:35:58.289082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
과세년도1.0000.0000.0000.0000.3130.0000.000
세목명0.0001.0000.2950.3860.1270.5140.347
체납액구간0.0000.2951.0000.0000.7520.1280.605
체납건수0.0000.3860.0001.0000.5850.9060.617
체납금액0.3130.1270.7520.5851.0000.4700.936
누적체납건수0.0000.5140.1280.9060.4701.0000.651
누적체납금액0.0000.3470.6050.6170.9360.6511.000
2024-01-10T07:35:58.379378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
체납액구간세목명
체납액구간1.0000.156
세목명0.1561.000
2024-01-10T07:35:58.474603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도체납건수체납금액누적체납건수누적체납금액세목명체납액구간
과세년도1.0000.1240.2760.0670.1980.0000.000
체납건수0.1241.0000.3820.9690.4170.2400.000
체납금액0.2760.3821.0000.2960.9430.0660.466
누적체납건수0.0670.9690.2961.0000.3960.3140.057
누적체납금액0.1980.4170.9430.3961.0000.1800.327
세목명0.0000.2400.0660.3140.1801.0000.156
체납액구간0.0000.0000.4660.0570.3270.1561.000

Missing values

2024-01-10T07:35:55.854849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T07:35:55.978800image/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
0충청남도서천군447702017등록면허세10만원 미만717761801911923090
1충청남도서천군447702017자동차세10만원 미만138616996072430128020
2충청남도서천군447702017자동차세10만원~30만원미만1572594555053689057960
3충청남도서천군447702017자동차세30만원~50만원미만51845880176160870
4충청남도서천군447702017재산세10만원 미만4336404030180626279430
5충청남도서천군447702017재산세10만원~30만원미만102031470336104770
6충청남도서천군447702017재산세1백만원~3백만원미만238182101018629970
7충청남도서천군447702017재산세30만원~50만원미만266422051684320
8충청남도서천군447702017재산세3백만원~5백만원미만13989870415258770
9충청남도서천군447702017재산세50만원~1백만원미만43090990118391870
시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
139충청남도서천군447702022지방소득세1천만원~3천만원미만3459144706107185050
140충청남도서천군447702022지방소득세30만원~50만원미만1765226703212534170
141충청남도서천군447702022지방소득세3백만원~5백만원미만14058660936245600
142충청남도서천군447702022지방소득세50만원~1백만원미만19139505004835920510
143충청남도서천군447702022지방소득세5백만원~1천만원미만2107472301171477270
144충청남도서천군447702022취득세10만원 미만31531106247020
145충청남도서천군447702022취득세10만원~30만원미만2333440101458150
146충청남도서천군447702022취득세1백만원~3백만원미만1189668011896680
147충청남도서천군447702022취득세30만원~50만원미만13038501303850
148충청남도서천군447702022취득세50만원~1백만원미만15798801579880