Overview

Dataset statistics

Number of variables10
Number of observations44
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.8 KiB
Average record size in memory89.0 B

Variable types

Categorical6
Numeric4

Dataset

Description지방자치단체는 지방세 체납액 규모별 체납 건수를 납세자 유형별로 파악하여 지방자치단체 지방세 체납 정책을 수립시 기초자료로 활용하고자 함
URLhttps://www.data.go.kr/data/15078598/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 체납건수High correlation
누적체납금액 is highly overall correlated with 체납건수 and 1 other fieldsHigh correlation
체납금액 has unique valuesUnique
누적체납금액 has unique valuesUnique

Reproduction

Analysis started2023-12-12 09:21:55.703631
Analysis finished2023-12-12 09:21:58.254055
Duration2.55 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size484.0 B
대구광역시
44 

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 44
100.0%

Length

2023-12-12T18:21:58.320888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:21:58.430412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대구광역시 44
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size484.0 B
동구
44 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
동구 44
100.0%

Length

2023-12-12T18:21:58.536202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:21:58.667387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동구 44
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size484.0 B
27140
44 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row27140
2nd row27140
3rd row27140
4th row27140
5th row27140

Common Values

ValueCountFrequency (%)
27140 44
100.0%

Length

2023-12-12T18:21:58.785894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:21:58.906048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
27140 44
100.0%

과세년도
Categorical

CONSTANT 

Distinct1
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size484.0 B
2022
44 

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 44
100.0%

Length

2023-12-12T18:21:59.041503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:21:59.166409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 44
100.0%

세목명
Categorical

Distinct7
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Memory size484.0 B
재산세
10 
취득세
10 
지방소득세
9 
주민세
5 
자동차세
4 
Other values (2)
6 

Length

Max length7
Median length3
Mean length3.9545455
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
재산세 10
22.7%
취득세 10
22.7%
지방소득세 9
20.5%
주민세 5
11.4%
자동차세 4
 
9.1%
지역자원시설세 4
 
9.1%
등록면허세 2
 
4.5%

Length

2023-12-12T18:21:59.286105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:21:59.423914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
재산세 10
22.7%
취득세 10
22.7%
지방소득세 9
20.5%
주민세 5
11.4%
자동차세 4
 
9.1%
지역자원시설세 4
 
9.1%
등록면허세 2
 
4.5%

체납액구간
Categorical

Distinct10
Distinct (%)22.7%
Missing0
Missing (%)0.0%
Memory size484.0 B
10만원 미만
7 
10만원~30만원미만
7 
50만원~1백만원미만
6 
30만원~50만원미만
5 
1백만원~3백만원미만
4 
Other values (5)
15 

Length

Max length11
Median length11
Mean length10.318182
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
10만원 미만 7
15.9%
10만원~30만원미만 7
15.9%
50만원~1백만원미만 6
13.6%
30만원~50만원미만 5
11.4%
1백만원~3백만원미만 4
9.1%
3백만원~5백만원미만 4
9.1%
1천만원~3천만원미만 3
6.8%
3천만원~5천만원미만 3
6.8%
5백만원~1천만원미만 3
6.8%
5천만원~1억원미만 2
 
4.5%

Length

2023-12-12T18:21:59.605491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:21:59.788598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10만원 7
13.7%
미만 7
13.7%
10만원~30만원미만 7
13.7%
50만원~1백만원미만 6
11.8%
30만원~50만원미만 5
9.8%
1백만원~3백만원미만 4
7.8%
3백만원~5백만원미만 4
7.8%
1천만원~3천만원미만 3
5.9%
3천만원~5천만원미만 3
5.9%
5백만원~1천만원미만 3
5.9%

체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)63.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean794.84091
Minimum1
Maximum14548
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-12T18:21:59.948369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median9.5
Q3111.5
95-th percentile3307.8
Maximum14548
Range14547
Interquartile range (IQR)108.5

Descriptive statistics

Standard deviation2513.7768
Coefficient of variation (CV)3.1626163
Kurtosis22.185767
Mean794.84091
Median Absolute Deviation (MAD)8.5
Skewness4.4908149
Sum34973
Variance6319074
MonotonicityNot monotonic
2023-12-12T18:22:00.111603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1 7
 
15.9%
3 5
 
11.4%
5 4
 
9.1%
8 2
 
4.5%
9 2
 
4.5%
12 2
 
4.5%
1056 1
 
2.3%
72 1
 
2.3%
6 1
 
2.3%
10 1
 
2.3%
Other values (18) 18
40.9%
ValueCountFrequency (%)
1 7
15.9%
2 1
 
2.3%
3 5
11.4%
5 4
9.1%
6 1
 
2.3%
8 2
 
4.5%
9 2
 
4.5%
10 1
 
2.3%
12 2
 
4.5%
13 1
 
2.3%
ValueCountFrequency (%)
14548 1
2.3%
7534 1
2.3%
3324 1
2.3%
3216 1
2.3%
2654 1
2.3%
1128 1
2.3%
1056 1
2.3%
340 1
2.3%
306 1
2.3%
167 1
2.3%

체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct44
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86346958
Minimum139410
Maximum5.5245043 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-12T18:22:00.269889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum139410
5-th percentile435194.5
Q16540330
median49268895
Q31.2498623 × 108
95-th percentile3.0324818 × 108
Maximum5.5245043 × 108
Range5.5231102 × 108
Interquartile range (IQR)1.184459 × 108

Descriptive statistics

Standard deviation1.1557591 × 108
Coefficient of variation (CV)1.3385058
Kurtosis6.7340997
Mean86346958
Median Absolute Deviation (MAD)48510660
Skewness2.3947368
Sum3.7992662 × 109
Variance1.335779 × 1016
MonotonicityNot monotonic
2023-12-12T18:22:00.448394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
37295710 1
 
2.3%
117414760 1
 
2.3%
42105440 1
 
2.3%
65573940 1
 
2.3%
124010380 1
 
2.3%
69559950 1
 
2.3%
81274250 1
 
2.3%
294690 1
 
2.3%
425170 1
 
2.3%
1353930 1
 
2.3%
Other values (34) 34
77.3%
ValueCountFrequency (%)
139410 1
2.3%
294690 1
2.3%
425170 1
2.3%
492000 1
2.3%
563750 1
2.3%
610910 1
2.3%
680300 1
2.3%
836170 1
2.3%
1353930 1
2.3%
1856040 1
2.3%
ValueCountFrequency (%)
552450430 1
2.3%
436368570 1
2.3%
317552780 1
2.3%
222188770 1
2.3%
193144310 1
2.3%
172313710 1
2.3%
149226900 1
2.3%
143957210 1
2.3%
133796860 1
2.3%
128214710 1
2.3%

누적체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)70.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1821.2955
Minimum1
Maximum38214
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-12T18:22:00.600320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.15
Q15
median14
Q3185.75
95-th percentile9043.5
Maximum38214
Range38213
Interquartile range (IQR)180.75

Descriptive statistics

Standard deviation6250.47
Coefficient of variation (CV)3.4318814
Kurtosis27.929803
Mean1821.2955
Median Absolute Deviation (MAD)12
Skewness5.0192427
Sum80137
Variance39068376
MonotonicityNot monotonic
2023-12-12T18:22:00.754896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
5 5
 
11.4%
3 3
 
6.8%
1 3
 
6.8%
12 2
 
4.5%
2 2
 
4.5%
8 2
 
4.5%
15 2
 
4.5%
4 2
 
4.5%
110 1
 
2.3%
170 1
 
2.3%
Other values (21) 21
47.7%
ValueCountFrequency (%)
1 3
6.8%
2 2
 
4.5%
3 3
6.8%
4 2
 
4.5%
5 5
11.4%
7 1
 
2.3%
8 2
 
4.5%
10 1
 
2.3%
12 2
 
4.5%
13 1
 
2.3%
ValueCountFrequency (%)
38214 1
2.3%
13496 1
2.3%
9105 1
2.3%
8695 1
2.3%
3906 1
2.3%
2164 1
2.3%
2047 1
2.3%
600 1
2.3%
451 1
2.3%
325 1
2.3%

누적체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct44
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4386852 × 108
Minimum239410
Maximum1.4665941 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-12T18:22:00.909554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum239410
5-th percentile486820
Q112106140
median75817450
Q31.5476109 × 108
95-th percentile5.6006732 × 108
Maximum1.4665941 × 109
Range1.4663547 × 109
Interquartile range (IQR)1.4265495 × 108

Descriptive statistics

Standard deviation2.5369325 × 108
Coefficient of variation (CV)1.7633687
Kurtosis17.356845
Mean1.4386852 × 108
Median Absolute Deviation (MAD)68844845
Skewness3.7940011
Sum6.330215 × 109
Variance6.4360267 × 1016
MonotonicityNot monotonic
2023-12-12T18:22:01.108028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
72472490 1
 
2.3%
181126140 1
 
2.3%
68066210 1
 
2.3%
93987480 1
 
2.3%
124010380 1
 
2.3%
131347730 1
 
2.3%
88949650 1
 
2.3%
323460 1
 
2.3%
425170 1
 
2.3%
1353930 1
 
2.3%
Other values (34) 34
77.3%
ValueCountFrequency (%)
239410 1
2.3%
323460 1
2.3%
425170 1
2.3%
836170 1
2.3%
1353930 1
2.3%
1500850 1
2.3%
2626660 1
2.3%
3509010 1
2.3%
4526400 1
2.3%
6293170 1
2.3%
ValueCountFrequency (%)
1466594140 1
2.3%
635822880 1
2.3%
561845440 1
2.3%
549991330 1
2.3%
396360610 1
2.3%
193144310 1
2.3%
181126140 1
2.3%
172313710 1
2.3%
169829990 1
2.3%
164170810 1
2.3%

Interactions

2023-12-12T18:21:57.473081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:21:55.984042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:21:56.382713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:21:56.901688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:21:57.570159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:21:56.071429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:21:56.514015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:21:57.100934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:21:57.687479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:21:56.167868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:21:56.648418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:21:57.210502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:21:57.815767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:21:56.262457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:21:56.763875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:21:57.346376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:22:01.249686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명체납액구간체납건수체납금액누적체납건수누적체납금액
세목명1.0000.0000.4110.4120.4110.294
체납액구간0.0001.0000.0000.2360.0000.000
체납건수0.4110.0001.0000.9511.0000.902
체납금액0.4120.2360.9511.0000.9510.935
누적체납건수0.4110.0001.0000.9511.0000.902
누적체납금액0.2940.0000.9020.9350.9021.000
2023-12-12T18:22:01.365185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명체납액구간
세목명1.0000.000
체납액구간0.0001.000
2023-12-12T18:22:01.831480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
체납건수체납금액누적체납건수누적체납금액세목명체납액구간
체납건수1.0000.5160.9580.5870.2650.000
체납금액0.5161.0000.3930.9770.2380.071
누적체납건수0.9580.3931.0000.4980.2650.000
누적체납금액0.5870.9770.4981.0000.1810.000
세목명0.2650.2380.2650.1811.0000.000
체납액구간0.0000.0710.0000.0000.0001.000

Missing values

2023-12-12T18:21:57.996952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:21:58.182640image/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
0대구광역시동구271402022등록면허세10만원 미만105637295710204772472490
1대구광역시동구271402022등록면허세10만원~30만원미만11394102239410
2대구광역시동구271402022자동차세10만원 미만33241439572109105396360610
3대구광역시동구271402022자동차세10만원~30만원미만321655245043086951466594140
4대구광역시동구271402022자동차세30만원~50만원미만16757981170325112334520
5대구광역시동구271402022자동차세50만원~1백만원미만156375084526400
6대구광역시동구271402022재산세10만원 미만753431755278013496549991330
7대구광역시동구271402022재산세10만원~30만원미만26544363685703906635822880
8대구광역시동구271402022재산세1백만원~3백만원미만7912821471095153249100
9대구광역시동구271402022재산세1천만원~3천만원미만914922690010159297050
시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
34대구광역시동구271402022취득세10만원 미만16680300351500850
35대구광역시동구271402022취득세10만원~30만원미만101856040417652040
36대구광역시동구271402022취득세1백만원~3백만원미만685210101218832340
37대구광역시동구271402022취득세1천만원~3천만원미만81723137108172313710
38대구광역시동구271402022취득세30만원~50만원미만83340860156293170
39대구광역시동구271402022취득세3백만원~5백만원미만520560940520560940
40대구광역시동구271402022취득세3천만원~5천만원미만31337968603133796860
41대구광역시동구271402022취득세50만원~1백만원미만1276068202215552500
42대구광역시동구271402022취득세5백만원~1천만원미만526749780526749780
43대구광역시동구271402022취득세5천만원~1억원미만31931443103193144310