Overview

Dataset statistics

Number of variables10
Number of observations41
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 KiB
Average record size in memory89.2 B

Variable types

Categorical6
Numeric4

Dataset

Description세목별 체납액구간, 체납건수, 체납금액, 누적체납건수, 누적체납금액을 제공하여 체납정책 등을 수립하기 위한 기초자료로 활용할 수 있습니다.
URLhttps://www.data.go.kr/data/15080383/fileData.do

Alerts

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

Reproduction

Analysis started2023-12-12 04:04:45.533421
Analysis finished2023-12-12 04:04:48.933169
Duration3.4 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size460.0 B
대구광역시
41 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대구광역시 41
100.0%

Length

2023-12-12T13:04:49.024474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:04:49.180302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대구광역시 41
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size460.0 B
북구
41 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
북구 41
100.0%

Length

2023-12-12T13:04:49.333319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:04:49.475480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
북구 41
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size460.0 B
27230
41 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row27230
2nd row27230
3rd row27230
4th row27230
5th row27230

Common Values

ValueCountFrequency (%)
27230 41
100.0%

Length

2023-12-12T13:04:49.641891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:04:49.779735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
27230 41
100.0%

과세년도
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size460.0 B
2022
41 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 41
100.0%

Length

2023-12-12T13:04:49.915901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:04:50.081467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 41
100.0%

세목명
Categorical

Distinct7
Distinct (%)17.1%
Missing0
Missing (%)0.0%
Memory size460.0 B
재산세
11 
지방소득세
10 
취득세
9 
주민세
5 
자동차세
4 
Other values (2)
2 

Length

Max length7
Median length3
Mean length3.7317073
Min length3

Unique

Unique2 ?
Unique (%)4.9%

Sample

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

Common Values

ValueCountFrequency (%)
재산세 11
26.8%
지방소득세 10
24.4%
취득세 9
22.0%
주민세 5
12.2%
자동차세 4
 
9.8%
등록면허세 1
 
2.4%
지역자원시설세 1
 
2.4%

Length

2023-12-12T13:04:50.231601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:04:50.416680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
재산세 11
26.8%
지방소득세 10
24.4%
취득세 9
22.0%
주민세 5
12.2%
자동차세 4
 
9.8%
등록면허세 1
 
2.4%
지역자원시설세 1
 
2.4%

체납액구간
Categorical

Distinct11
Distinct (%)26.8%
Missing0
Missing (%)0.0%
Memory size460.0 B
10만원 미만
7 
10만원~30만원미만
5 
30만원~50만원미만
5 
50만원~1백만원미만
5 
1백만원~3백만원미만
4 
Other values (6)
15 

Length

Max length11
Median length11
Mean length10.170732
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 7
17.1%
10만원~30만원미만 5
12.2%
30만원~50만원미만 5
12.2%
50만원~1백만원미만 5
12.2%
1백만원~3백만원미만 4
9.8%
1천만원~3천만원미만 3
7.3%
3백만원~5백만원미만 3
7.3%
5백만원~1천만원미만 3
7.3%
1억원~3억원미만 2
 
4.9%
3천만원~5천만원미만 2
 
4.9%

Length

2023-12-12T13:04:50.640087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10만원 7
14.6%
미만 7
14.6%
10만원~30만원미만 5
10.4%
30만원~50만원미만 5
10.4%
50만원~1백만원미만 5
10.4%
1백만원~3백만원미만 4
8.3%
1천만원~3천만원미만 3
6.2%
3백만원~5백만원미만 3
6.2%
5백만원~1천만원미만 3
6.2%
1억원~3억원미만 2
 
4.2%
Other values (2) 4
8.3%

체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)73.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean870.82927
Minimum1
Maximum14085
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T13:04:50.818698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median13
Q3179
95-th percentile3698
Maximum14085
Range14084
Interquartile range (IQR)175

Descriptive statistics

Standard deviation2510.8936
Coefficient of variation (CV)2.8833363
Kurtosis20.024268
Mean870.82927
Median Absolute Deviation (MAD)12
Skewness4.222882
Sum35704
Variance6304586.7
MonotonicityNot monotonic
2023-12-12T13:04:51.018937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1 8
 
19.5%
4 3
 
7.3%
3 2
 
4.9%
10 2
 
4.9%
1014 1
 
2.4%
5 1
 
2.4%
20 1
 
2.4%
7 1
 
2.4%
6 1
 
2.4%
21 1
 
2.4%
Other values (20) 20
48.8%
ValueCountFrequency (%)
1 8
19.5%
3 2
 
4.9%
4 3
 
7.3%
5 1
 
2.4%
6 1
 
2.4%
7 1
 
2.4%
8 1
 
2.4%
9 1
 
2.4%
10 2
 
4.9%
13 1
 
2.4%
ValueCountFrequency (%)
14085 1
2.4%
6738 1
2.4%
3698 1
2.4%
3590 1
2.4%
3031 1
2.4%
1498 1
2.4%
1014 1
2.4%
407 1
2.4%
327 1
2.4%
232 1
2.4%

체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0726745 × 108
Minimum203700
Maximum6.4654349 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T13:04:51.215315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum203700
5-th percentile1951120
Q17025380
median74390830
Q31.3127517 × 108
95-th percentile3.4207785 × 108
Maximum6.4654349 × 108
Range6.4633979 × 108
Interquartile range (IQR)1.2424979 × 108

Descriptive statistics

Standard deviation1.3288205 × 108
Coefficient of variation (CV)1.238792
Kurtosis7.0424582
Mean1.0726745 × 108
Median Absolute Deviation (MAD)67365450
Skewness2.3923696
Sum4.3979653 × 109
Variance1.765764 × 1016
MonotonicityNot monotonic
2023-12-12T13:04:51.420869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
34267030 1
 
2.4%
203700 1
 
2.4%
205958300 1
 
2.4%
149947150 1
 
2.4%
66870230 1
 
2.4%
158953330 1
 
2.4%
113540110 1
 
2.4%
125908280 1
 
2.4%
152665120 1
 
2.4%
69357670 1
 
2.4%
Other values (31) 31
75.6%
ValueCountFrequency (%)
203700 1
2.4%
471630 1
2.4%
1951120 1
2.4%
2091670 1
2.4%
2600070 1
2.4%
3668650 1
2.4%
4061700 1
2.4%
4106750 1
2.4%
4941900 1
2.4%
5897000 1
2.4%
ValueCountFrequency (%)
646543490 1
2.4%
485925910 1
2.4%
342077850 1
2.4%
236363050 1
2.4%
228249240 1
2.4%
205958300 1
2.4%
158953330 1
2.4%
156654890 1
2.4%
152665120 1
2.4%
149947150 1
2.4%

누적체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)78.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1323.0488
Minimum1
Maximum18928
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T13:04:51.616938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median20
Q3227
95-th percentile7996
Maximum18928
Range18927
Interquartile range (IQR)221

Descriptive statistics

Standard deviation3617.8131
Coefficient of variation (CV)2.7344518
Kurtosis14.476585
Mean1323.0488
Median Absolute Deviation (MAD)19
Skewness3.620822
Sum54245
Variance13088572
MonotonicityNot monotonic
2023-12-12T13:04:51.795390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1 7
 
17.1%
6 2
 
4.9%
18 2
 
4.9%
13 2
 
4.9%
211 1
 
2.4%
149 1
 
2.4%
14 1
 
2.4%
202 1
 
2.4%
45 1
 
2.4%
3 1
 
2.4%
Other values (22) 22
53.7%
ValueCountFrequency (%)
1 7
17.1%
2 1
 
2.4%
3 1
 
2.4%
6 2
 
4.9%
7 1
 
2.4%
9 1
 
2.4%
10 1
 
2.4%
11 1
 
2.4%
13 2
 
4.9%
14 1
 
2.4%
ValueCountFrequency (%)
18928 1
2.4%
9650 1
2.4%
7996 1
2.4%
7524 1
2.4%
4108 1
2.4%
1882 1
2.4%
1460 1
2.4%
550 1
2.4%
408 1
2.4%
389 1
2.4%

누적체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4923823 × 108
Minimum366280
Maximum1.3811987 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T13:04:51.950986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum366280
5-th percentile3652090
Q110265740
median87851270
Q31.5799831 × 108
95-th percentile4.9061525 × 108
Maximum1.3811987 × 109
Range1.3808324 × 109
Interquartile range (IQR)1.4773257 × 108

Descriptive statistics

Standard deviation2.4039265 × 108
Coefficient of variation (CV)1.610798
Kurtosis17.56922
Mean1.4923823 × 108
Median Absolute Deviation (MAD)74629530
Skewness3.8018317
Sum6.1187676 × 109
Variance5.7788626 × 1016
MonotonicityNot monotonic
2023-12-12T13:04:52.151711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
48544100 1
 
2.4%
366280 1
 
2.4%
257339470 1
 
2.4%
213544710 1
 
2.4%
80104880 1
 
2.4%
175193250 1
 
2.4%
113540110 1
 
2.4%
148254170 1
 
2.4%
157998310 1
 
2.4%
69357670 1
 
2.4%
Other values (31) 31
75.6%
ValueCountFrequency (%)
366280 1
2.4%
1040860 1
2.4%
3652090 1
2.4%
4023550 1
2.4%
4692290 1
2.4%
4803240 1
2.4%
6111970 1
2.4%
7025380 1
2.4%
7128730 1
2.4%
10030300 1
2.4%
ValueCountFrequency (%)
1381198670 1
2.4%
658573320 1
2.4%
490615250 1
2.4%
326222750 1
2.4%
303050510 1
2.4%
257339470 1
2.4%
254391490 1
2.4%
213544710 1
2.4%
175193250 1
2.4%
162480800 1
2.4%

Interactions

2023-12-12T13:04:47.991290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:45.920753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:46.487194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:47.414411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:48.126825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:46.064282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:47.014305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:47.568354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:48.265638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:46.199503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:47.136695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:47.704712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:48.408074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:46.328928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:47.273027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:04:47.869526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:04:52.281545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명체납액구간체납건수체납금액누적체납건수누적체납금액
세목명1.0000.0000.1790.3510.1890.507
체납액구간0.0001.0000.0000.0000.0000.000
체납건수0.1790.0001.0000.7800.9020.827
체납금액0.3510.0000.7801.0000.9010.916
누적체납건수0.1890.0000.9020.9011.0000.994
누적체납금액0.5070.0000.8270.9160.9941.000
2023-12-12T13:04:52.432068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명체납액구간
세목명1.0000.000
체납액구간0.0001.000
2023-12-12T13:04:52.561569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
체납건수체납금액누적체납건수누적체납금액세목명체납액구간
체납건수1.0000.5430.9880.6030.0920.000
체납금액0.5431.0000.4850.9870.1150.000
누적체납건수0.9880.4851.0000.5540.0910.000
누적체납금액0.6030.9870.5541.0000.1230.000
세목명0.0920.1150.0910.1231.0000.000
체납액구간0.0000.0000.0000.0000.0001.000

Missing values

2023-12-12T13:04:48.613863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:04:48.858540image/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
0대구광역시북구272302022등록면허세10만원 미만101434267030146048544100
1대구광역시북구272302022자동차세10만원 미만35901566548907524326222750
2대구광역시북구272302022자동차세10만원~30만원미만369864654349079961381198670
3대구광역시북구272302022자동차세30만원~50만원미만23281099400408141457370
4대구광역시북구272302022자동차세50만원~1백만원미만949419001810030300
5대구광역시북구272302022재산세10만원 미만67383420778509650490615250
6대구광역시북구272302022재산세10만원~30만원미만30314859259104108658573320
7대구광역시북구272302022재산세1백만원~3백만원미만149236363050162254391490
8대구광역시북구272302022재산세1억원~3억원미만11185701601118570160
9대구광역시북구272302022재산세1천만원~3천만원미만81312751709141887030
시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
31대구광역시북구272302022지역자원시설세10만원 미만620370018366280
32대구광역시북구272302022취득세10만원 미만7471630201040860
33대구광역시북구272302022취득세10만원~30만원미만204106750316111970
34대구광역시북구272302022취득세1백만원~3백만원미만474281401119752060
35대구광역시북구272302022취득세1억원~3억원미만11295108101129510810
36대구광역시북구272302022취득세1천만원~3천만원미만126461540126461540
37대구광역시북구272302022취득세30만원~50만원미만51951120104023550
38대구광역시북구272302022취득세3백만원~5백만원미만1406170027128730
39대구광역시북구272302022취득세50만원~1백만원미만4260007063652090
40대구광역시북구272302022취득세5백만원~1천만원미만1702538017025380